Datasets:

---

suriyagunasekar

/

stackoverflow-python-with-meta-data

like 12

[ "I think you need merge by bout number in level of MultiIndex with index in bt_df:\nmain_df = (bd_df.reset_index()\n .merge(bt_df, \n left_on='bout number',\n right_index=True, \n how='left', \n suffixes=('_',''))\n .set_index(['name_', 'bout number'])\n )\n\n\nprint (main_df)\n outcome opp_name name country colour wins \\\nname_ bout number \nSam 3 win Roy Jones James USA green 12 \n 2 win Floyd Mayweather Dyaus Arsenal white 3 \n 1 win Bernard Hopkins Jonny China blue 9 \nJames 3 win James Bond James USA green 12 \n 2 win Michael O'Terry Dyaus Arsenal white 3 \n 1 win Donald Trump Jonny China blue 9 \nJonny 3 win Oscar De la Hoya James USA green 12 \n 2 win Roberto Duran Dyaus Arsenal white 3 \n 1 loss Manny Pacquiao Jonny China blue 9 \nDyaus 3 win Thierry Henry James USA green 12 \n 2 win David Beckham Dyaus Arsenal white 3 \n 1 loss Gabriel Jesus Jonny China blue 9 \n\n losses \nname_ bout number \nSam 3 6 \n 2 8 \n 1 3 \nJames 3 6 \n 2 8 \n 1 3 \nJonny 3 6 \n 2 8 \n 1 3 \nDyaus 3 6 \n 2 8 \n 1 3 \n\n" ]

[ 1 ]

[]

[]

[ "dataframe", "merge", "multi_index", "pandas", "python" ]

[ "If you looking add smaller subplot of volume just below OHLC chart, you can use:\n\nrows and cols to specify the grid for subplots.\nshared_xaxes=True for same zoom and filtering\nrow_width=[0.2, 0.7] to change height ratio of charts. ie. smaller volume chart than OHLC\n\nPlot:\n\nimport pandas as pd\nimport plotly.graph_objects as go\nfrom plotly.subplots import make_subplots\n\n# data\ndf = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv')\n\n\n# Create subplots and mention plot grid size\nfig = make_subplots(rows=2, cols=1, shared_xaxes=True, \n vertical_spacing=0.03, subplot_titles=('OHLC', 'Volume'), \n row_width=[0.2, 0.7])\n\n# Plot OHLC on 1st row\nfig.add_trace(go.Candlestick(x=df[\"Date\"], open=df[\"AAPL.Open\"], high=df[\"AAPL.High\"],\n low=df[\"AAPL.Low\"], close=df[\"AAPL.Close\"], name=\"OHLC\"), \n row=1, col=1\n)\n\n# Bar trace for volumes on 2nd row without legend\nfig.add_trace(go.Bar(x=df['Date'], y=df['AAPL.Volume'], showlegend=False), row=2, col=1)\n\n# Do not show OHLC's rangeslider plot \nfig.update(layout_xaxis_rangeslider_visible=False)\nfig.show()\n\n", "You haven't provided a complete code snippet with a data sample, so I'm going to have to suggest a solution that builds on an example here.\nIn any case, you're getting that error message simply because go.Candlestick does not have a Volume attribute. And it might not seem so at first, but you can easily set up go.Candlestick as an individual trace, and then include an individual go.Bar() trace for Volumes using:\n\nfig = make_subplots(specs=[[{\"secondary_y\": True}]])\nfig.add_traces(go.Candlestick(...), secondary_y=True)\nfig.add_traces(go.Bar(...), secondary_y=False)\n\nPlot:\n\nComplete code:\nimport plotly.graph_objects as go\nfrom plotly.subplots import make_subplots\nimport pandas as pd\n\n# data\ndf = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv')\n\n# Create figure with secondary y-axis\nfig = make_subplots(specs=[[{\"secondary_y\": True}]])\n\n# include candlestick with rangeselector\nfig.add_trace(go.Candlestick(x=df['Date'],\n open=df['AAPL.Open'], high=df['AAPL.High'],\n low=df['AAPL.Low'], close=df['AAPL.Close']),\n secondary_y=True)\n\n# include a go.Bar trace for volumes\nfig.add_trace(go.Bar(x=df['Date'], y=df['AAPL.Volume']),\n secondary_y=False)\n\nfig.layout.yaxis2.showgrid=False\nfig.show()\n\n", "Here is my improvement implementation based on the previous answer by Vestland, with some labelling and colouring improvements.\nimport plotly.graph_objects as go\nfrom plotly.subplots import make_subplots\n\ncandlesticks = go.Candlestick(\n x=candles.index,\n open=candles['open'],\n high=candles['high'],\n low=candles['low'],\n close=candles['close'],\n showlegend=False\n)\n\nvolume_bars = go.Bar(\n x=candles.index,\n y=candles['volume'],\n showlegend=False,\n marker={\n \"color\": \"rgba(128,128,128,0.5)\",\n }\n)\n\nfig = go.Figure(candlesticks)\nfig = make_subplots(specs=[[{\"secondary_y\": True}]])\nfig.add_trace(candlesticks, secondary_y=True)\nfig.add_trace(volume_bars, secondary_y=False)\nfig.update_layout(title=\"ETH/USDC pool after Uniswap v3 deployment\", height=800)\nfig.update_yaxes(title=\"Price $\", secondary_y=True, showgrid=True)\nfig.update_yaxes(title=\"Volume $\", secondary_y=False, showgrid=False)\nfig.show()\n\n\nYou can find the full source code in this open-source notebook.\n", "If you want to add different colors for buy/sell isay 'green'/'red', you can use some libs (e.g. mplfinance) which do these automatically however the plots are non-interactive. To get interactive plot with plotly with separate colors for buy/sell colors, one needs to add trace for each data point. Here is code:\n import plotly.graph_objects as go\n from plotly.subplots import make_subplots\n import pandas as pd\n # Create subplots and mention plot grid size\n title=df.symbol.unique()[0]\n\n fig = make_subplots(rows=2, cols=1, shared_xaxes=True, \n vertical_spacing=0.02, \n row_width=[0.25, 0.75])\n\n # Plot OHLC on 1st row\n fig.add_trace(go.Candlestick(x=df.index,\n open=df['open'], high=df['high'],\n low=df['low'], close=df['close'],showlegend=False),row=1, col=1,)\n\n # Bar trace for volumes on 2nd row without legend\n # fig.add_trace(go.Bar(x=df.index, y=df['volume'], showlegend=False), row=2, col=1)\n\n df['color']=''\n df['color']=['red' if (x>y) else t for x,y,t in zip(df['open'],df['close'],df['color'])]\n df['color']=['green' if (x<y) else t for x,y,t in zip(df['open'],df['close'],df['color'])]\n colors=df.color.tolist()\n df['prev_color']=[colors[0]]+colors[:(len(colors)-1)]\n df.loc[((df.open==df.close) & (df.color=='')),'color']=[z for x,y,z,t in zip(df['open'],df['close'],df['prev_color'],df['color']) if (x==y and t=='')]\n colors=df.color.tolist()\n df['prev_color']=[colors[0]]+colors[:(len(colors)-1)]\n df.loc[((df.open==df.close) & (df.color=='')),'color']=[z for x,y,z,t in zip(df['open'],df['close'],df['prev_color'],df['color']) if (x==y and t=='')]\n \n markers=['green','red']\n\n for t in markers:\n df_tmp=df.loc[~(df.color==t)] ## somehow the color it takes is opposite so take negation to \n fig.add_trace(go.Bar(x=df_tmp.index, y=df_tmp['volume'], showlegend=False), row=2, col=1)\n\n # Do not show OHLC's rangeslider plot \n fig.update(layout_xaxis_rangeslider_visible=False)\n fig.layout.yaxis2.showgrid=False\n fig.update_layout(title_text=title,title_x=0.45)\n\n fig.show()\n\n", "My two cents on Plotting Volume in a different subplot with colors, it is just making @user6397960 response shorter without hacks to get the right color, just use marker_color. Think about it, what makes a candle green? The fact of having Close price above the Open price, and what about red candle? well, having a close price below the open price, so with this basics:\nimport plotly.graph_objects as go\nfrom plotly.subplots import make_subplots\n\n# Create a Figure with 2 subplots, one will contain the candles\n# the other will contain the Volume bars\nfigure = make_subplots(rows=2, cols=1, shared_xaxes=True, row_heights=[0.7, 0.3])\n\n# Plot the candles in the first subplot\nfigure.add_trace(go.Candlestick(x=df.index, open=df.open, high=df.high, low=df.low, close=df.close, name='price',\n increasing_line_color='#26a69a', decreasing_line_color='#ef5350'),\n row=1, col=1)\n\n# From our Dataframe take only the rows where the Close > Open\n# save it in different Dataframe, these should be green\ngreen_volume_df = df[df['close'] > df['open']]\n# Same for Close < Open, these are red candles/bars\nred_volume_df = df[df['close'] < df['open']]\n\n# Plot the red bars and green bars in the second subplot\nfigure.add_trace(go.Bar(x=red_volume_df.index, y=red_volume_df.volume, showlegend=False, marker_color='#ef5350'), row=2,\n col=1)\nfigure.add_trace(go.Bar(x=green_volume_df.index, y=green_volume_df.volume, showlegend=False, marker_color='#26a69a'),\n row=2, col=1)\n\n# Hide the Range Slider\nfigure.update(layout_xaxis_rangeslider_visible=False)\nfigure.update_layout(title=f'BTC/USDT', yaxis_title=f'Price')\nfigure.update_yaxes(title_text=f'Volume', row=2, col=1)\nfigure.update_xaxes(title_text='Date', row=2)\n\nReferences\n\nhttps://plotly.com/python/subplots/\nhttps://plotly.com/python/candlestick-charts/\n\n" ]

[ 24, 23, 2, 1, 0 ]

[]

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[ "matplotlib", "plot", "plotly", "python" ]

[ "It turned out being cache question - no idea what created a site-package cache under ~/./local/ and why python looked there at first\n" ]

[ 0 ]

[]

[]

[ "pandas", "python", "python_3.x" ]

[ "I have almost the same issue.\nNot a good idea - remove part of the list that you are iterating.\nTry this:\nfrom PyInstaller.building.datastruct import TOC\n\n# ...\n# a = Analysis(...)\n\nx = 'cp36-win_amd64'\ndatas_upd = TOC()\n\nfor d in a.datas:\n if x not in d[0] and x not in d[1]:\n datas_upd.append(d)\n\na.datas = datas_upd\n\n\n", "Going to put this here in case it helps anyone, since I spent some time finding out how to do this.\nin the .spec of your pyinstaller project, add this after the line a = Analysis(...):\n# Avoid warning\nto_remove = [\"_AES\", \"_ARC4\", \"_DES\", \"_DES3\", \"_SHA256\", \"_counter\"]\nfor b in a.binaries:\n found = any(\n f'{crypto}.cp37-win_amd64.pyd' in b[1]\n for crypto in to_remove\n )\n if found:\n print(f\"Removing {b[1]}\")\n a.binaries.remove(b)\n\nOf course you may adapt the array to_remove as well as the exact file name .cp37-win_amd64.pyd to match the files that show up in your warnings.\nThis results in the files not being included in the .exe and the warnings are gone.\n", "As good as it is to encounter the warning message, noting them and creating an iterable used to exclude them in the .spec file, it would be even better if we don't have to pass through that stress procedure. Let's try it.\nObservation: The data structure of both the 'datas' and 'binaries' are the same within the .spec file. i.e [(module_or_file_name, absolute_path, type_DATA_or_BINARY), ...]\nThe methods here are the same, the implementation is what's different. We look for what has been added to a.datas and repeated/re-added into a.binaries\nMethod 1: [1-liner but slower]\n# ...\n# a = Analysis(...)\n\na.binaries = [b for b in a.binaries if not b in list(set(b for d in a.datas for b in a.binaries if b[1].endswith(d[0])))] # The unique binaries not repeated in a.datas.\n\nMethod 2: [faster]\n# ...\n# a = Analysis(...)\n\nfor b in a.binaries.copy(): # Traver the binaries.\n for d in a.datas: # Traverse the datas.\n if b[1].endswith(d[0]): # If duplicate found.\n a.binaries.remove(b) # Remove the duplicate.\n break\n\nI used this implementation when I was creating a simplified combined power of Cython + PyInstaller + AES encryption GUI Bundling application.\nHope this helps someone in future.\n" ]

[ 1, 0, 0 ]

[]

[]

[ "pyinstaller", "python" ]

[ "You could select the find all numerics instead of trying to remove non-numerics\nIn any case you have to make some assumtpions about the input, here is the code assuming that a final block of two digits in a text with separators is the fractional part.\nimport re\n\nmy_list = ['100,30 Kč','10 000,00 Kč', '10,000.00 Kč', '10000 Kč', '32.100,30 Kč', '12.345,678.91 Kč']\n\nfor s in my_list:\n parts = list(re.findall('\\d+', s))\n if len(parts) == 1 or len(parts[-1]) != 2:\n parts.append('0')\n print(float(''.join(parts[:-1]) + '.' + parts[-1]))\n\n", "This should do the job.\ndef parse_entry(entry):\n\n #remove currency and spaces\n entry = entry.replace(\"Kč\", \"\")\n entry = entry.replace(\" \", \"\")\n\n #check if a comma is used for decimals or thousands\n comma_i = entry.find(\",\")\n if len(entry[comma_i:]) > 3: #it's a thousands separator, it can be removed\n entry = entry.replace(\",\", \"\")\n else: #it's a decimal separator\n entry = entry.replace(\",\", \".\") #convert it to dot\n\n #remove extra dots\n while entry.count(\".\") > 1:\n entry = entry.replace(\".\", \"\", 1) #replace once\n return round(float(entry), 1) #round to 1 decimal\n\nmy_list = ['100,30 Kč','10 000,00 Kč', '10,000.00 Kč', '10000 Kč', '32.100,30 Kč', '12.345,678.91 Kč']\nparsed = list(map(parse_entry, my_list))\nprint(parsed) #[100.3, 10000.0, 10000.0, 10000.0, 32100.3, 12345678.9]\n\n", "I tried to keep your code and add just few lines. The idea is the store in a variable the number after \".\" and then add it after replacing the \",\" with \".\" and join the number separated by \".\".\nimport re\n\nmy_list = ['100,30 Kč','10 000,00 Kč', '10,000.00 Kč', '10000 Kč', '32.100,30 Kč', '12.345,678.91 Kč']\n\nfor i in my_list:\n ws = i.replace(\"Kč\", '')\n x = re.sub(',','.', ws).replace(\" \",\"\")\n \n if len( x.split(\".\"))>1:\n end= x.split(\".\")[-1]\n x = \"\".join([i for i in x.split(\".\")[:-1]])+\".\"+end\n print(x)\n\n", "Whilst the other answers work for your specific scenario (e.g. you know the current code you're replacing), it's not very extensible.\nSo here's a more generic approach:\nimport re\n\nvalues = [\n \"100,30 Kč\",\n \"10 000,00 Kč\",\n \"10,000.00 Kč\",\n \"10000 Kč\",\n \"32.100,30 Kč\",\n \"12.345,678.91 Kč\", # This value is a bit odd... is it _right_?\n]\n\nfor value in values:\n # Remove any character that's not a number or a comma\n value = re.sub(\"[^0-9,]\", \"\", value)\n\n # Replace remaining commas with periods\n value = value.replace(\",\", \".\")\n\n # Convert from string to number\n value = float(value)\n\n print(value)\n\nThis outputs:\n100.3\n10000.0\n10.0\n10000.0\n32100.3\n12345.67891\n\n", "Without the aid or re you could just do this:\nmy_list = ['100,30 Kč','10 000,00 Kč', '10,000.00 Kč', '10000 Kč', '32.100,30 Kč', '12.345,678.91 Kč']\n\ndef fix(s):\n r = []\n for c in s:\n if c in '0123456789':\n r.append(c)\n elif c == ',':\n r.append('.')\n elif not c in '. ':\n break\n return float(''.join(r))\n\nfor n in my_list:\n print(fix(n))\n\nOutput:\n100.3\n10000.0\n10.0\n10000.0\n32100.3\n12345.67891\n\n" ]

[ 1, 0, 0, 0, 0 ]

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[]

[ "python", "python_re" ]

[ "So, I commented heavily about how unsure I am that it is really the thing to do, and how I suspect XY problem on this.\nBut, just to give a formal answer to the question, as is, I repeat here what I said in comments:\nnp.frombuffer(contents, dtype=np.uint8)\n\nIs the way to turn a byte string into a numpy array of bytes (that is of uint8 integers)\nThe dtype part is important. Since frombuffer does not just iterates bytes to create an array, but expect to find the data representation as is in the buffer. And without the dtype it will try to create an array of float64 from your buffer. Which, 7 times out of 8, will fail because an array of float64 can be represented only by buffers of bytes of len multiple of 8. And if len of contents happen to be multiple of 8, it will succeeds, giving your meaningless floats.\nFor example, on a .mp4 of mine\nwith open('out.mp4', 'rb') as f:\n content=f.read()\n\nlen(content)\n# 63047 - it is a very small mp4\n\nx=np.frombuffer(content)\n# ValueError: buffer size must be a multiple of element size\n\nx=np.frombuffer(content[:63040])\nx.shape\n# (7880,)\nx.dtype\n# np.float64\nx[:10]\n#array([ 6.32301702e+233, 2.78135139e-309, 9.33260821e-066,\n# 1.15681581e-071, 2.78106620e+180, 3.98476928e+252,\n# nan, 9.02529811e+042, -3.58729431e+222,\n# 1.08615058e-153])\n\nx=np.frombuffer(content, dtype=np.uint8)\nx.shape\n# (63047,)\nx.dtype\n# uint8\nx[:10]\n# array([ 0, 0, 0, 32, 102, 116, 121, 112, 105, 115], dtype=uint8)\n\n" ]

[ 0 ]

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[ "binaryfiles", "mp4", "numpy", "python" ]

[ "It should work, but only after you shutdown the API.\nLogging inside the main function before calling uvicorn.run() and inside endpoint routes should work as you expected.\nuvicorn.run() is a sync function. So the interpreter waits until the function has finished (API has shutdown) and executes the following statements afterwards.\n" ]

[ 1 ]

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[ "fastapi", "logging", "python", "python_3.x", "uvicorn" ]

[ "List comprehension works, but not optimised way to solve these sort of problems\nIn [48]: list(set([j[0] for j in matches if j[0] not in [i[1] for i in matches]]))\nOut[48]: [1, 2, 10]\n\n", "What you did\nneverLost = [w for w, l in matches if w not l]\n\nis going to check whether the first item in that tuple is equal to the second item in that same tuple. It is not checking whether the first item is present at 2nd position in any other tuple.\nYou can use this -\nlist(set([w[0] for w in matches if w[0] not in [l[1] for l in matches]]))\n\n", "I think you need to do something like,\nmatches = [[1, 3], [2, 3], [3, 6], [5, 6], [5, 7], [4, 5], [4, 8], [4, 9], [10, 4], [10, 9]]\n\nlosses_dict = {}\nfor (_, value) in matches:\n losses_dict.setdefault(value, 0) # key might exist already\n losses_dict[value] += 1\n\nfinal_list = [\n [k for k, _ in matches if k not in losses_dict.keys()],\n [k for k, v in losses_dict.items() if v == 1]\n ]\n\n\nThe dict.setdefault checks if the key already existis otherwise it appends the value 0.\n", "Sure, something like this is a reasonable solution:\nseconds = {second for _, second in matches}\nnever_lost = [first for first, _ in matches if first not in seconds]\nnever_lost = list(dict.fromkeys(never_lost)) # idiom for removing duplicates while maintaining order\n\nNow, if you mean only using a single list comprehension expression, then you'd have to get pretty janky:\nnever_lost = [\n x for x in\n {\n first: None\n for first, _ in matches \n for seconds in [{second for _, second in matches}] \n if first not in seconds\n }\n]\n\nI'd stick to the first approach. Not everything has to or should be a single list comprehension expression.\n", "neverLost = list(set(w for w, l in matches if all(w != j for i, j in matches)))\n\nList comprehensions are good for performance but most of the time complex list comprehensions can decrease code readability.\n", "Another approach is to model the problem as a directed graph where each player is a vertex of the graph and edge determines result of the match, such that edge direction is from winner to loser.\nfor eg.\n\n[1, 2] => [1](winner) -> [2](loser)\n\nNow problem boils down to calculating in-degree/out-degree of the vertices, which should give us the desired result.\n" ]

[ 2, 1, 1, 1, 0, 0 ]

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[]

[ "list", "list_comprehension", "python" ]

[ "After reviewing the source code for the sklearn CountVectorizer class, particularly the fit function, I don't believe the class has any way of tracking the original document element indexes relative to the extracted unigram features: where the unigram features do not necessarily have the same tokens. Other than the simple solution provided below, you might have to rely on some other method/library to achieve your desired results. If there is a particular case that fails, I'd suggest adding that to your question as it might help people generate solutions to your problem.\nfrom sklearn.feature_extraction.text import CountVectorizer\n\nvectorizer_unigram = CountVectorizer(analyzer='word', ngram_range=(1, 1), stop_words = 'english')\n\ndoc = {'sent': ['Two women are embracing while holding to go packages .'],\n 'tags': ['NUM NOUN AUX VERB SCONJ VERB PART VERB NOUN PUNCT']}\n\nsentence = vectorizer_unigram.fit(doc['sent'])\nsentence_unigrams = sentence.get_feature_names_out()\n\nsent_token_list = doc['sent'][0].split()\ntags_token_list = doc['tags'][0].split()\nsentence_tags = []\n\nfor unigram in sentence_unigrams:\n for i in range(len(sent_token_list)):\n if sent_token_list[i] == unigram:\n sentence_tags.append(tags_token_list[i])\n\nprint(sentence_unigrams)\n# Output: ['embracing' 'holding' 'packages' 'women']\nprint(sentence_tags)\n# Output: ['VERB', 'VERB', 'NOUN', 'NOUN']\n\n" ]

[ 0 ]

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[ "countvectorizer", "nlp", "python", "scikit_learn", "stanford_nlp" ]

[ "If you want to be able to come back to a previous state (before the fight), either store the previous state, or use the Command pattern which allows for easy \"undo\", or do something else that may require re-architecturing your game.\nIt would be simpler to help you if we had a Minimal Reproducible Example of your problem.\n", "It is good to start a problem like this with the data structures.\nYou might want a player, rooms and mobs. YAML is handy for this kind of data modeling.\nMODEL = \"\"\"\nrooms:\n 0:\n desc: You are in the lobby on Shang Tsung's Island. It is a rather grey room.\n name: The lobby\n 1:\n desc: An aged Shang Tsung sits on his throne and watches the fights below. Monk\n students also watch and applaud after every round is finished.\n name: The courtyard\n 2:\n desc: A prominent lava river flows in the background. On the west side of the\n stageis a bridge that goes over it, while the east side leads to a split road.\n A rock outcropping marks each side of the stage. Spikes and their victims, dot\n the landscape.\n name: Krossroads\n 3:\n desc: An sandy oval arena. From the stands giant drummers beat their drums as the \n fighters beat each other up. It is said that in the event of a tie, Konga, \n the biggest drummer of them all, will challenge both of the warriors.\n name: Drum Arena\nplayer:\n location: 0\n health: 24\nmobs:\n 0:\n name: Kobra\n location: 1\n desc: Kobra is a tall man. The newest recruit of the Black Dragon has \n shoulder length blond hair and he wears a white karate gi with black \n and gold trim. He wears no shirt under his gi. He has taped wrists \n and gloves, and he wears dark baggy pants with yellow trim over bare feet.\n health: 14\n strength: 6\n 1:\n name: Kira\n location: 3\n desc: Kira is a rational level-headed anarchist, opposite in character to Kobra.\n She has unlikely red hair in a pair of even more unlikely pony tails and\n wears a black leather jerkin and leggings dyed in the blood of her\n many victims. In each hand she wields a serrated dagger.\n health: 20\n strength: 8\n\"\"\"\n\nThe game is then structured around this data model. At heart it a while loop that continues until the player runs out of health. At the start of the loop prompt the user for a command.\n\ndef run():\n ....\n while player['health'] > 0:\n cmd = prompt(\"cmd: \")\n if cmd == ...\n\nMore concretely, handle the command \"quit\" or \"q\" to allow the player to exit the game:\nimport yaml\nfrom rich import print\nfrom rich.prompt import Prompt\nfrom rich.panel import Panel\nimport random\n\ndef run():\n model = yaml.load(MODEL, Loader=yaml.Loader)\n rooms = model['rooms']\n player = model['player']\n while player['health'] > 0:\n loc = rooms[player['location']]\n prompt = f\"[orange1 on grey23][{loc['name']}][/]\"\n cmd = Prompt.ask(prompt).lower()\n if cmd in [\"q\", \"quit\"]:\n print(\"Thank you for playing\")\n return\n\nAdd commands to look, to move north and south.\n if cmd in [\"l\", \"look\"]:\n desc = [loc['desc']]\n print(Panel(\"\\n\".join(desc)))\n continue\n if cmd in [\"n\", \"north\"]:\n player['location'] += 1\n continue\n elif cmd in [\"s\", \"south\"]:\n player['location'] -= 1\n continue\n\nA complete example that incorporates mobs and a combat system.\ndef find_mob(model):\n mobs = model['mobs']\n player = model['player']\n for mob in mobs.values():\n if mob['location'] == player['location']:\n return mob\n\ndef run():\n print(Panel(\"Welcome to MORTAL TEXT BASED KOMBAT\\n\"\n \"Commands are north, south, look, punch and kick.\\n\"\n \"Good luck!\"))\n model = yaml.load(MODEL, Loader=yaml.Loader)\n rooms = model['rooms']\n player = model['player']\n loc = rooms[player['location']]\n while player['health'] > 0:\n new_loc = rooms[player['location']]\n if loc != new_loc:\n print(f\"[deep_sky_blue3 on thistle1] You enter {new_loc['name']}[/]\")\n loc = new_loc\n mob = find_mob(model)\n if mob:\n prompt = f\"[red3 on light_salmon1][{loc['name']} | You ({player['health']}) vs {mob['name']} ({mob['health']})] [/]\"\n else:\n prompt = f\"[orange1 on grey23][{loc['name']}][/]\"\n cmd = Prompt.ask(prompt).lower()\n if cmd in [\"n\", \"north\"]:\n if player['location'] < max(rooms.keys()):\n player['location'] += 1\n else:\n print(\"Sorry, you can't go north :worried: \")\n continue\n elif cmd in [\"s\", \"south\"]:\n if player['location'] > 0:\n player['location'] -= 1\n else:\n print(\"Sorry, you can't go south :worried: \")\n continue\n elif cmd in [\"l\", \"look\"]:\n desc = [loc['desc']]\n if mob:\n desc.append(f\"[red3 on grey93]{mob['name']} is here![/]\")\n desc.append(f\"[blue on white]{mob['desc']}[/]\")\n print(Panel(\"\\n\".join(desc)))\n continue\n if mob:\n hit, damage = -1, 0\n if cmd in [\"punch\", \"p\"]:\n cmd = \"punch\"\n hit, damage = random.randint(4,20), random.randint(1,6)\n elif cmd in [\"kick\", \"k\"]:\n cmd = \"kick\"\n hit, damage = random.randint(1,20), random.randint(2,8)\n if hit > 9:\n print(f\"Your {cmd} hits and does {damage} damage!\")\n mob[\"health\"] -= damage\n if mob['health'] <= 0:\n print(f\"You killed {mob['name']}!\")\n mob['location'] = -1\n continue\n elif hit > 0:\n print(f\"Your {cmd} misses.\")\n if random.randint(1,20) > 10:\n damage = random.randint(1,mob['strength'])\n print(f\"{mob['name']} hits you. You take {damage} damage.\")\n player['health'] -= damage\n if player['health'] <= 0:\n print(\"[red on black]You died[/] :skull:\")\n\n" ]

[ 0, 0 ]

[]

[]

[ "function", "python", "return", "text" ]

[ "You can use the key parameter of concat to increment a step based on the max id in the original DataFrame:\nN = 4\n\nstep = df['id'].max()\nout = pd.concat([df]*N, keys=range(N))\nout['id'] += out.index.get_level_values(0)*step\nout = out.droplevel(0)\n\nMore simple variant with numpy:\nimport numpy as np\n\nN = 4\n\nstep = df['id'].max()\na = np.repeat(np.arange(N), len(df))\nout = pd.concat([df]*N)\nout['id'] += a*step\n\noutput:\n id code1 code2 date\n0 1 a0 aa 2022-10-01\n1 3 b0 bb 2022-09-01\n2 500 b0 cc 2022-10-01\n3 53 c0 bb 2022-11-01\n4 1 b0 cc 2022-09-01\n5 500 a0 bb 2022-11-01\n0 501 a0 aa 2022-10-01 # new id starts at 501\n1 503 b0 bb 2022-09-01\n2 1000 b0 cc 2022-10-01\n3 553 c0 bb 2022-11-01\n4 501 b0 cc 2022-09-01\n5 1000 a0 bb 2022-11-01\n0 1001 a0 aa 2022-10-01 # new id starts at 1001\n1 1003 b0 bb 2022-09-01\n2 1500 b0 cc 2022-10-01\n3 1053 c0 bb 2022-11-01\n4 1001 b0 cc 2022-09-01\n5 1500 a0 bb 2022-11-01\n0 1501 a0 aa 2022-10-01 # new id starts at 1501\n1 1503 b0 bb 2022-09-01\n2 2000 b0 cc 2022-10-01\n3 1553 c0 bb 2022-11-01\n4 1501 b0 cc 2022-09-01\n5 2000 a0 bb 2022-11-01\n\n" ]

[ 2 ]

[]

[]

[ "dataframe", "pandas", "python", "repeat" ]

[ "Here you can see the relevant documentation. Change the line\nplt.xticks(rotation = 30,weight='bold',fontsize=60)\n\nto\nplt.xticks(ticks = list(range(0,70_000,10_000)),rotation = 30,weight='bold',fontsize=60)\n\nand you will have ticks at only every 10 thousand steps. You can change the list to suit your needs.\n" ]

[ 1 ]

[]

[]

[ "jupyter_notebook", "pandas", "python" ]

[ "You can't \"execute everything in it\" in one go, since there are three different functions.\nYou'd do\nvm = vraagmachine() # instantiate a `vraagmachine`; calls __init__\nvm.askname()\nvm.askage()\n\nif that's the order you want to ask things in, or the other way around maybe.\n" ]

[ 0 ]

[]

[]

[ "call", "class", "python" ]

[ "Once you patched the target class, anything you try to access under that class will be mocked with MagicMock (also recursively). Therefore, if you want to keep the specification of that class, then yes, you should use the autospec=true flag.\nBut because you are trying to mock a class within a class accessed by a property,\nYou have to keep the specification of each class you want to test:\ndef test_class_a():\n class_b_mock = create_autospec(class_a.ClassB)\n class_c_mock = create_autospec(class_b.ClassC)\n class_b_mock.class_c = class_c_mock\n with mock.patch(\"class_a.ClassB\", return_value=class_b_mock):\n class_a_instance = ClassA()\n with pytest.raises(TypeError):\n class_a_instance.class_b.from_config()\n with pytest.raises(TypeError):\n class_a_instance.class_b.class_c.delete()\n\n" ]

[ 4 ]

[]

[]

[ "mocking", "python", "unit_testing" ]

[ "Comment out line 8.\nAdd this parameter orient=Tk.Vertical.\nslider = customtkinter.CTkSlider(master=win, from_=0, to=100,orient=Tk.Vertical, command=slider,fg_color='#555555',progress_color='#144870')\n\n" ]

[ 1 ]

[]

[]

[ "python", "slider", "tkinter" ]

[ "How about dividing each clothes by size(with quantity)? Clothes with different size can be treated as different product.\nproduct\n\n\n\n\nID\nname\nimage\ndescription\nprice\n...\n\n\n\n\n1\njean\na.jpg\ngood jean\n12345\n...\n\n\n\n\nsize\n\n\n\n\nID\nproduct_id\nsize\nquantity\n...\n\n\n\n\n1\n1\nxxxl\n12345\n...\n\n\n2\n1\nxxl\n1234\n...\n\n\n3\n1\nxl\n123\n...\n\n\n\n\nIf quantity is greater than 0, that size of clothes is available.\n", "my solution is:\nforms.py\nfrom django import forms\nfrom django.forms import ModelForm\nfrom shop.models import Product\n\n\nclass CartAddProductForm(ModelForm):\n class Meta:\n model = Product\n fields = ['sizes']\n\n def __init__(self, pk, *args, **kwargs):\n super(CartAddProductForm, self).__init__(*args, **kwargs)\n sizes = tuple(Product.objects.get(pk=pk).sizes)\n sizes_list = []\n for item in sizes:\n sizes_list.append((item, item))\n self.fields['sizes'] = forms.ChoiceField(choices=sizes_list)\n\nwhen I create the form, I pass the pk:\nviews.py\nproduct = get_object_or_404(Product,\n id=product_id,\n slug=product_slug,\n available=True)\npk = product.pk\ncart_product_form = CartAddProductForm(instance=product, pk=pk)\n\n" ]

[ 0, 0 ]

[]

[]

[ "django", "django_4.1", "python" ]

[ "Try to use re.sub to replace the match:\nimport re\n\ntext = \"\"\"\\\nQuestions and Answers\nOperator [1]\n\nShannon Siemsen Cross, Cross Research LLC - Co-Founder, Principal & Analyst [2]\nI hope everyone is well. Tim, you talked about seeing some improvement in the second half of April. So I was wondering if you could just talk maybe a bit more on the segment and geographic basis what you're seeing in the various regions that you're selling in and what you're hearing from your customers. And then I have a follow-up.\nTimothy D. Cook, Apple Inc. - CEO & Director [3]\"\"\"\n\ntext = re.sub(r\".*\\d]\", \"\", text)\nprint(text)\n\nPrints:\nQuestions and Answers\n\n\n\nI hope everyone is well. Tim, you talked about seeing some improvement in the second half of April. So I was wondering if you could just talk maybe a bit more on the segment and geographic basis what you're seeing in the various regions that you're selling in and what you're hearing from your customers. And then I have a follow-up.\n\n", "The first argument to re.sub is treated as a regular expression, so the square brackets get a special meaning and don't match literally.\nYou don't need a regular expression for this replacement at all though (and you also don't need the loop counter i):\nfor name_line in name_lines:\n text = text.replace(name_line, '')\n\n" ]

[ 1, 0 ]

[]

[]

[ "python", "python_re", "regex" ]

[ "for i in range(len(table)):\n for j in range(len(table[i])):\n if (i+j)%2 == 0:\n table[i][j] = 0\n\noutput:\n [[0, 1, 0, 1, 0],\n [1, 0, 1, 0, 1],\n [0, 1, 0, 1, 0],\n [1, 0, 1, 0, 1],\n [0, 1, 0, 1, 0]]\n\n" ]

[ 2 ]

[ "There doesn't appear to be any reliance on the original values in the list. Therefore it might be better to implement something that creates a list in the required format like this:\ndef checkboard(rows, columns):\n e = 0\n result = []\n for _ in range(rows):\n c = []\n for _ in range(columns):\n c.append(e)\n e ^= 1\n result.append(c)\n return result\n \nprint(checkboard(5, 5))\nprint(checkboard(2, 3))\nprint(checkboard(4, 4))\n\nOutput:\n[[0, 1, 0, 1, 0], [1, 0, 1, 0, 1], [0, 1, 0, 1, 0], [1, 0, 1, 0, 1], [0, 1, 0, 1, 0]]\n[[0, 1, 0], [1, 0, 1]]\n[[0, 1, 0, 1], [0, 1, 0, 1], [0, 1, 0, 1], [0, 1, 0, 1]]\n\n" ]

[ -1 ]

[ "python" ]

[ "I think because you're creating a new instance instead of actually updating an existing record, after fetching your record via its id start changing the field values needed and then invoke the save method.\npost_edit.body=\"Example\"\npost_edit.save()\n\n" ]

[ 0 ]

[]

[]

[ "flask", "flask_sqlalchemy", "flask_wtforms", "python" ]

[ "Had a shot at it, but as expected the weak background contrast is giving trouble, as does the directed lighting. Just checking the stone right now, but the script should give you the tools to find the two reference cards as well. If you want to show the intermediate images, see the comments in the script.\nDo you have the original image in another format then JPG by chance ? The color compression in the file is really not helping with extraction.\n\nimport cv2\n\n# get image\nimg = cv2.imread(\"<YouPathHere>\")\n\n# extract stone shadow in RGB\nchB, chG, chR = cv2.split(img)\n\nthreshShadow = 48\nimgChanneldiff = chR-chB\nimgshadow = cv2.threshold(imgChanneldiff,threshShadow,255,cv2.THRESH_BINARY)[1]\n#cv2.namedWindow(\"imgshadow\", cv2.WINDOW_NORMAL)\n#cv2.imshow(\"imgshadow\", imgshadow)\n\n# extract stone together with shadow in HSV\nimgHSV = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)\nthreshHue = 10\nchH, chS, chV = cv2.split(imgHSV)\nimgbin = cv2.threshold(chH,threshHue,255,cv2.THRESH_BINARY)[1]\n#cv2.namedWindow(\"imgbin\", cv2.WINDOW_NORMAL)\n#cv2.imshow(\"imgbin\", imgbin)\n\n\nimgResultMask = imgbin - imgshadow\nMorphKernelSize = 25;\nimgResultMask = cv2.erode(imgResultMask, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, [MorphKernelSize,MorphKernelSize]))\nimgResultMask = cv2.dilate(imgResultMask, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, [MorphKernelSize,MorphKernelSize]))\ncv2.namedWindow(\"imgResultMask\", cv2.WINDOW_NORMAL)\ncv2.imshow(\"imgResultMask\", imgResultMask)\n\ncontours = cv2.findContours(imgResultMask,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)[0]\ncv2.drawContours(img, contours, -1, (255,125,0), 3)\n\nfor c in contours:\n cv2.rectangle(img, cv2.boundingRect(c), (0,125,255))\n\ncv2.namedWindow(\"img\", cv2.WINDOW_NORMAL)\ncv2.imshow(\"img\", img)\n\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n\n" ]

[ 2 ]

[]

[]

[ "computer_vision", "opencv", "python" ]

[ "\nThe Selenium WebElement object received by driver.find_element(ByLocator) is already a reference to the actual physical web element on the page. In other words, the WebElement object is an address of the actual web element you asking about.\nThere is no way to get a By locator of an already found WebElement\n\nSo, in your particular example temp = br.find_element(By.LINK_TEXT, (str(day_to_book))) the temp is an address of the element you can keep for future use (until the page is changed / refreshed)\n" ]

[ 0 ]

[]

[]

[ "python", "selenium", "xpath" ]

[ "You can use a MultiIndex:\n(df.set_axis(pd.MultiIndex\n .from_arrays([df.columns,\n df.groupby(df.columns, axis=1)\n .cumcount()]),\n axis=1)\n .loc[0].unstack().add_prefix('value_')\n)\n\nOutput:\n value_0 value_1\nAddressLine1 2201 W WILLOW 2201 W WILLOW\nAddressLine123 2201 W WILLOW 2201 W WILLOW\nCity ENID ENID\nCityStateZip ENID, OK 73073 ENID, OK 73073\nPostalCode 73073 73073\nSSN 12345678 NaN\n\nIf you have several rows in the input and want to keep them:\n(df.set_axis(pd.MultiIndex\n .from_arrays([df.columns,\n df.groupby(df.columns, axis=1)\n .cumcount()]),\n axis=1)\n .stack(0).add_prefix('value_')\n)\n\nOutput:\n value_0 value_1\n0 AddressLine1 2201 W WILLOW 2201 W WILLOW\n AddressLine123 2201 W WILLOW 2201 W WILLOW\n City ENID ENID\n CityStateZip ENID, OK 73073 ENID, OK 73073\n PostalCode 73073 73073\n SSN 12345678 NaN\n\n" ]

[ 1 ]

[]

[]

[ "dataframe", "pandas", "python" ]

[ "You need to change the default opener for the .py files on your computer.\ntry to right-click on the .py file, select \"open with\" and look for python.\nYou can always use cmd and run the following:\npython <path_to_file>\n\nor\npython3 <path_to_file>\n\nIt depended on your python version\n(as long as you set your python path in windows Path)\n" ]

[ 0 ]

[]

[]

[ "anaconda", "python", "windows" ]

[ "As commented by Paul Brodersen the image used for the mask has to be black and white, with black corresponding to the area to be filled.\nThanks Paul\n" ]

[ 0 ]

[]

[]

[ "matplotlib", "python", "word_cloud" ]

[ "Cause of Error\n\nThe error is your global statements should be inside function software since the global keyword allows us to modify variables outside of the current scope. You need it in the function software\n\nImprovements\n\nCoding is largely about using the right data structures. Using a variable for each question and answer makes it difficult to loop over the questions and answers. It's better to use a list in this case.\nNo need for global for this simple program (globals are normally unnecessary and frowned upon when not needed).\n\nuse a mutable argument (such as a dictionary) to store points\n\n\nUnnecessarily to use f-strings in all your print statements. Unnecessary usage confuses the reader since they are looking for a variable in your string.\nsoftware is a meaningless name for a function\n\nuse something more descriptive of its purpose such as update_points\n\n\n\nImproved Code\n#The error is your global statements should be inside function software since the global keyword allows us to modify variables outside of the current scope. You need it in \n# The questions and answers should be in a data structure rather than each being its own variable\n# This allows you to interate over the questions\n# Most of your print statements you can use string literal rather than f-strings since they don't use a variable\n# Unnecessarily using f-strings confuses the code reviewer since they are looking for a variable in your string\n#QUESTION\n\nquestions = [\"TRUE OR FALSE: The lightest atom is hydrogen\",\n \"TRUE OR FALSE: Osmium is one of the densest atom if not the most\",\n \"TRUE OR FALSE: chemistry is the branch of science that deals with the identification of the substances of which matter is composed; the investigation of their properties and the ways in which they interact, combine, and change; and the use of these processes to form new substances.\",\n \"TRUE OR FALSE: There are 200 elements discovered in history\",\n \"TRUE OR FALSE: Dmitri Ivanovich Mendeleev was a Russian chemist and inventor. He is best known for formulating the Periodic Law and creating a version of the periodic table of elements.\",\n \"TRUE OR FALSE: The earth is the center of the earth\",\n \"TRUE OR FALSE: The sun the the center of the earth\",\n \"TRUE OR FALSE: The sun makes up most of the mass in our solar system\",\n \"TRUE OR FALSE: Jupiter has the most moon in the solar system\",\n \"TRUE OR FALSE: Mars's nick name is the red planet\",\n \"TRUE OR FALSE: Supernova's are the biggest explosions in the universe\",\n \"TRUE OR FALSE: Gamma rays bursts are the biggest explosions the universe\"\n \"TRUE OR FALSE: All stars turn into supernovas\",\n \"TRUE OR FALSE: All stars turn into blacks holes\",\n \"TRUE OR FALSE: All stars turn into neutron stars\",\n \"TRUE OR FALSE: Based on the model, Juptier is the biggest planet\",\n \"TRUE OR FALSE: Based on the model, earth is the samllest planet\",\n \"TRUE OR FALSE: Based on the model, neptune is the farthest planet to the sun\",\n \"TRUE OR FALSE: Based on the model, the sun is the biggest object\",\n \"TRUE OR FALSE: Based on the model the asteroid belt is located between Jupiter and Mars\"]\n #____________________________________________________________________\n #ANSWER KEY\nanswers = [\"True\",\n \"True\",\n \"True\",\n \"False\",\n \"True\",\n \"False\",\n \"True\",\n \"True\",\n \"False\",\n \"True\",\n \"False\",\n \"True\",\n \"False\",\n \"False\",\n \"False\",\n \"True\",\n \"False\",\n \"True\",\n \"True\",\n \"True\"]\n\ndef update_points(points, key):\n '''\n Updates points for player1 & player2\n \n '''\n print(p1, p2, key)\n if p1!=key and p2!=key:\n print(\"Both of you got it wrong\")\n \n elif p2==key and p1!=key:\n print(f\"{p2name} got it right\")\n # increment player 2 points\n points[p2name] += 100\n \n elif p1==key and p2!=key:\n \n print(f\"{p1name} got it right\")\n # increment player 1 points\n points[p1name] += 100\n \n elif p1==key and p2==key:\n print(\"both of you got it right\")\n # increment both players points\n points[p1name] += 100\n points[p2name] += 100\n \n return points # updated points\n\n\nstartkey = \"Start\"\nrulekey = \"Rules\"\n\nstart = input('''Hello to the tester 9000\n Type start to start\n type rules to know how to play!''').capitalize().strip()\n\nif start==rulekey:\n rule = input(\"You need aleast 2 players to play this game\\n\\neverytime you get the question right you get 100 points\\n\\n the person with the most points after 20 questions is the winner\\ntype start to start\")\n \nrule = \"Start\"\n \nif start==startkey or rule==startkey:\n p1name = input(\"what is your name player one: \")\n p2name = input(\"what is your name player two: \")\n \n # Initialize points for each player\n points = {}\n points[p1name] = 0\n points[p2name] = 0\n \n for i, q in enumerate(questions):\n # loop over each question in list. Enumerate provides the index of the quesiton\n print(f\"time for question {i}\")\n print(questions[i])\n\n # Get answers\n p1 = input(f\"{p1name} turn\").capitalize().strip()\n p2 = input(f\"{p2name} turn\").capitalize().strip()\n\n # Update points\n points = update_points(points, answers[i])\n\n print(f\"Player 1 points: {points[p1name]}\")\n print(f\"Player 2 points: {points[p2name]}\")\n\n" ]

[ 0 ]

[]

[]

[ "python" ]

[]

[]

[ "Use:\ndf.groupby(['type', 'year']).sum().groupby(level='type').plot.bar()\n\nOutput:\n\n\n\nAn interesting alternative:\n(df.pivot_table(index='year', columns='type', values='income', aggfunc='sum')\n .plot.bar(subplots=True, figsize=(4,10))\n)\n\nOutput:\n\n" ]

[ -1 ]

[ "pandas", "python" ]

[ "You can use arithmetic with a conversion to log10 to get the number of digits:\nN = 2 # number of digits to keep before decimal part\nout = df.div(10**np.floor(np.log10(df.abs())+1).sub(N))\n\nOutput:\n Lat Long\n0 -31.419333 -64.191251\n1 -31.314728 -64.170803\n\nIntermediate (number of digits):\nnp.floor(np.log10(df.abs())+1)\n\n Lat Long\n0 9.0 16.0\n1 9.0 9.0\n\n", "Another possible solution, which uses regex to replace the first two digits to the same digits plus .:\ndf.astype(str).replace(r'(^-?\\d{2})',r'\\1.', regex=True).astype(float)\n\nOutput:\n Lat Long\n0 -31.419333 -64.191251\n1 -31.314728 -64.170803\n\n" ]

[ 4, 1 ]

[]

[]

[ "pandas", "python" ]

[ "This should make it work.\nimport os\n\ndef deleteComments(file):\n try:\n my_file = open(file, 'r')\n data = my_file.read()\n clean = \"\"\n comments_count = 0\n for i in data.split('\\n'):\n if i[0] == \"#\":\n clean += i\n clean += '\\n'\n comments_count += 1\n else:\n pass\n name = os.path.basename(path)\n with open(\"clean-\" + name, \"w\") as f:\n f.write(clean)\n f.close()\n my_file.close()\n return comments_count\n\n except:\n print(\"An error occurred with accessing the files\")\n return file\n\n" ]

[ 0 ]

[]

[]

[ "python" ]

[ "The proper way of doing it using datetime module:\nrecord1 = [[datetime.strptime(d, '%Y/%m/%d').strftime('%d/%m/%Y'), v] for d, v in record1]\n\nThis converts it to datetime object, then formats it the way you intended\n>>> record1\n[['19/02/2020', 7.0], ['20/02/2020', 7.3], ['21/02/2020', 6.1]]\n\nThis would be a robust, explicit solution.\nBut... Some experiments showed that manipulating strings with split would be a faster solution:\nfrom datetime import datetime\nimport time\n\n\ndef convert_with_datetime(record1):\n return [[datetime.strptime(d, '%Y/%m/%d').strftime('%d/%m/%Y'), v] for d, v in record1]\n\ndef convert_with_split(record1):\n return [['/'.join(d.split('/')[::-1]), v] for d, v in record1]\n\nrecord1 = [[\"2020/02/19\", 7.0], [\"2020/02/20\", 7.3], [\"2020/02/21\", 6.1]] * 10**6\n\n\n\nstart = time.time()\nconvert_with_datetime(record1)\nend = time.time()\nprint('convert_with_datetime:', end - start)\n\nstart = time.time()\nconvert_with_split(record1)\nend = time.time()\nprint('convert_with_split:', end - start)\n\nThe result is:\nconvert_with_datetime: 12.311486959457397\nconvert_with_split: 1.3060288429260254\n\n" ]

[ 3 ]

[]

[]

[ "date", "nested_lists", "python" ]

[ "No, not an easy way. Even if you could share a lock, you'd then run into lock contention issues\nEither:\n\n(easiest, just requires an extra step later) have each process write to a separate, uniquely named log file and concatenate them afterwards if you need to.\n(harder, requires an extra process and communication) delegate writing the single log file to a single process that the other processes communicate with. (This is essentially what SyslogHandler does.)\n(worst, slow and lock-contented) have each process lock, open, write, close, unlock the file for each entry they want to write\n\n" ]

[ 1 ]

[]

[]

[ "cron_task", "locks", "multithreading", "python" ]

[ "It is possible that you have many python versions installed on your computer.\nYou probably need to pip install again the same package for the python version you are using with your Pycharm if you want them to work correctly.\nif you are not sure how to do that with CMD commands,\nyou can access your Pycharm, look for \"Python packages\" at the bottom, and then search for your package.\nYou can see there is this package is installed already or not.\nand if not - just hit the 'install' button.\nafter that, you are ready to go.\n" ]

[ 1 ]

[]

[]

[ "pycharm", "python", "versioning" ]

[ "seems like an issue caused by a numpy.\nimporting a specific version of numpy ( downgrading it to 1.21.4) should fix the problem\nimport numpy \nnumpy.__version__ \n'1.21.4' \n\nmake sure, you not importing numpy as np again afterwards before you assign your clastering model\n", "Instead of downgrading numpy, you can try this:\npip install -U threadpoolctl\n\nIt worked for me.\n" ]

[ 0, 0 ]

[ "Setting the minimum value in your range to a value greater than 1 will fix this problem\nEX: range(2,10)\n" ]

[ -1 ]

[ "k_means", "python" ]

[ "The following code works for me:\nurl = \"http://www.immobilienscout24.de/\"\ndriver.get(url)\n\ntime.sleep(10)\n\nelement = driver.execute_script(\"\"\"return document.querySelector('#usercentrics-root').shadowRoot.querySelector(\"button[data-testid='uc-accept-all-button']\")\"\"\")\nelement.click()\n\n" ]

[ 0 ]

[]

[]

[ "automation", "css_selectors", "python", "selenium", "shadow_dom" ]

[ "Python identifiers with non-ASCII characters are subject to NFKC normalisation(1), you can see the effect in the following code:\nimport unicodedata\nfor char in ['K', '', '', '', '', '', 'ᴷ']:\n normalised_char = unicodedata.normalize('NFKC', char)\n print(char, normalised_char, ord(normalised_char))\n\nThe output of that is:\nK K 75\n K 75\n Κ 922\n K 75\n K 75\n K 75\nᴷ K 75\n\nThis shows that all but one of those is the same identifier, which is why your assert passes (it's missing the one different identifier) and why most seem to be the same value. It's no different really to the following code, in which it is hopefully immediately clear what will happen:\na = '1'\na = '2'\nb = '3'\na = '4'\na = '5'\na = '6'\na = '7'\nassert a == a == a == a == a == a # passes\nprint(f'{a=}, {a=}, {b=}, {a=}, {a=}, {a=}') # a=7 a=7 b=3 a=7 a=7 a=7\n\n\nIn response to your update, specifically the text:\n\nWhat is the advantage of creating the possibility that two different Unicode strings are eventually considered not to be different if they are used as names of Python identifiers?\n\nMy own particular viewpoint as a developer is that I want to be able to look at code and understand it. That's not going to be easy when different code-points map to similar or even identical graphemes(2), such as with:\nΩ = 1\nΩ = 2\nΩ = Ω + Ω\nprint(Ω * Ω)\n\nWhat would you expect from that code? You set omega to one, then two. You then double it to four, and print the square which is sixteen. Easy, right?\nAnd, in actual fact, that's what you do get in Python, despite the fact that there are both omega and ohm characters in that code, and that's because they normalise to the same identifier. Were they not normalised, you would instead have the equivalent of:\nomega = 1\nohm = 2\nohm = omega + ohm\nprint(ohm * ohm)\n\nAnd this would output nine rather than sixteen. Best of luck debugging that when you can't see a difference between the omega and ohm identifiers :-)\nThere are also diacritics that can have different representations, such as ḋ:\n\nU+1e0b (Latin Small Letter D with Dot Above).\nU+0064, U+0307 (Latin Small Letter D, Combining dot above).\n\nAnd this may get even more complex where a base letter can have multiple diacritics such as ậ, ç̇, or ė́. The order of combining marks may be arbitrary, meaning that there could be many ways of representing the ậç̇ė́ variable (two by two by two gives eight, but there are potentially more since distinct code points also exist for \"half-accented\" characters like ç) .\nNo, I think I very much appreciate the normalisation that happens to Python identifiers :-)\n\n(1) From the Python docs about identifiers:\n\nAll identifiers are converted into the normal form NFKC while parsing; comparison of identifiers is based on NFKC.\n\n\n(2) You can think of graphemes as the basic unit of writing (like a letter), similar to phonemes being the basic unit of speech (like a sound). So the English grapheme c has at least two phonemes, the hard-c in cook and the soft-c in ice.\nAnd, making matters even more complex, cook shows that there is one phoneme (hard-c) giving two separate graphemes, c and k.\nNow think how much more complex it gets when you introduce every other language on the planet, I'm surprised the members of the Unicode consortium don't go absolutely insane :-)\n" ]

[ 8 ]

[]

[]

[ "python", "python_3.x", "unicode" ]

[ "So, you have X_train and X_test. These are both lists containing tuples. The values in the tuples (a, b) have some range, like 0 -> 1. In your graphs, these are the x and y coordinates of your dots.\nYou also have y_train and y_test. These are the known classifications of all the tuples in X_train and X_test. These values can be either 0 or 1, and none in between. If a dot in your graph is in the blue region, that means that the predicted value of that dot (a, b) is 0. If the dot is in the red region, this means the predicted value is 1.\n# if X_train is this\nX_train = [(0.0, 0.0), (0.1, 0.9), (0.9, 0.0), (1, 0.9)]\n\n# then y_train has to be this, for you chart\ny_train = [0, 0, 1, 1]\n\nIf you then train a classifier on this (but normally more data), then you can ask it any point (a, b) and it will tell you 0 or 1 (aka blue or red).\nSo for example I predict for a point (a, b) that it has not seen in X_train (aka something that is in X_test):\nresult = clf.predict([(0.2, 0.2)])\n\nresult then equals: [0]. This is because looking at your graph, assuming x-axis and y-axis range 0 -> 1. The tuple (0.2, 0,2) falls in the blue region.\nIt knows this because it has learned the blue red classification you see in your graph from X_train and X_test. So when it gets new tuples it sees on which region that dot falls and classifies it as 0 or 1, region blue or region red.\nTo summarize. The colored regions show what value will be predicted for any given tuple (a, b). The dot positions (in the scatter) are given by the values (a, b) in the tuple. a and b for the tuple are in range between 0->1. The color is not a range, but a classification 0 or 1.\nHopefully it helps, good luck!\n", "Well we can definitely determine which region the new point will lie in, but before we do that I want to call attention to something you're doing in your code that is going to come back to bite you.\nThis line right here X = StandardScaler().fit_transform(X) will come back to smack you harder than you know.\nRemember, the point of doing a StandardScaler is to standardize the data (0 mean, unit variance). Also remember that what you do on your training set you must do on your test set. The caveat here is the operations you perform on your test set will be learned from your training set. I'll give a condensed form of your code to help illustrate this.\nX, y = datasets[0]\n\n# Save an instance of the standard scaler so we can apply it to unknown values later\nstandard_scalar = StandardScaler()\nstandard_scalar.fit(X) # Fit the \"training data\"\n\nX = standard_scalar.transform(X) # Transform the data\n\n# You ended up with a good score because the transformation in your data was applied to the entire dataset, \"X\". But normally you'd transform the X_test\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=42)\n\n# Train the classifier\nclf = KNeighborsClassifier(3)\nclf.fit(X_train, y_train)\n\nscore = clf.score(X_test, y_test) # 0.925. Good score but let's see what happens next\n\n# This is where things will go wrong in your code.\n# Instead, make sure you transform the test point your want to calculate\n>>> clf.predict([[1,3]])\narray([0])\n\n>>> clf.predict(standard_scalar.transform([[1,3]]))\narray([1])\n\nSo even though you can use predict in both cases, you need to make sure to apply the transformation to the data point that you're inputting.\n", "Try this.\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.colors import ListedColormap\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.datasets import make_moons, make_circles, make_classification\nfrom sklearn.neural_network import MLPClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.svm import SVC\nfrom sklearn.gaussian_process import GaussianProcessClassifier\nfrom sklearn.gaussian_process.kernels import RBF\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\nfrom sklearn.inspection import DecisionBoundaryDisplay\n\nnames = [\n \"Nearest Neighbors\",\n]\n\nclassifiers = [\n KNeighborsClassifier(3),\n]\n\nX, y = make_classification(\n n_features=2, n_redundant=0, n_informative=2, random_state=1, n_clusters_per_class=1\n)\nrng = np.random.RandomState(2)\nX += 2 * rng.uniform(size=X.shape)\nlinearly_separable = (X, y)\n\ndatasets = [\n linearly_separable,\n]\n\nfigure = plt.figure(figsize=(27, 9))\ni = 1\n# iterate over datasets\nfor ds_cnt, ds in enumerate(datasets):\n # preprocess dataset, split into training and test part\n X, y = ds\n X = StandardScaler().fit_transform(X)\n X_train, X_test, y_train, y_test = train_test_split(\n X, y, test_size=0.4, random_state=42\n )\n\n x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5\n y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5\n\n # just plot the dataset first\n cm = plt.cm.RdBu\n cm_bright = ListedColormap([\"#FF0000\", \"#0000FF\"])\n ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n if ds_cnt == 0:\n ax.set_title(\"Input data\")\n # Plot the training points\n ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright, edgecolors=\"k\")\n # Plot the testing points\n ax.scatter(\n X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6, edgecolors=\"k\"\n )\n ax.set_xlim(x_min, x_max)\n ax.set_ylim(y_min, y_max)\n ax.set_xticks(())\n ax.set_yticks(())\n i += 1\n\n # iterate over classifiers\n for name, clf in zip(names, classifiers):\n ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n clf.fit(X_train, y_train)\n score = clf.score(X_test, y_test)\n All_Value_Response = DecisionBoundaryDisplay.from_estimator(\n clf, X, cmap=cm, alpha=0.8, ax=ax, eps=0.5\n )\n\n # Plot the training points\n ax.scatter(\n X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright, edgecolors=\"k\"\n )\n # Plot the testing points\n ax.scatter(\n X_test[:, 0],\n X_test[:, 1],\n c=y_test,\n cmap=cm_bright,\n edgecolors=\"k\",\n alpha=0.6,\n )\n \n X1 = All_Value_Response.xx0.ravel()\n Y1 = All_Value_Response.xx1.ravel()\n Color = All_Value_Response.response.ravel()\n \n Outputs = []\n \n for X2, Y2 in X:\n XD = X2 - X1\n YD = Y2 - Y1\n Distance = (XD * XD) + (YD * YD)\n Color_Gradient = Color[Distance.argmin()]\n Outputs.append(Color_Gradient)\n \n print(Outputs)\n ax.set_xlim(x_min, x_max)\n ax.set_ylim(y_min, y_max)\n ax.set_xticks(())\n ax.set_yticks(())\n if ds_cnt == 0:\n ax.set_title(name)\n ax.text(\n x_max - 0.3,\n y_min + 0.3,\n (\"%.2f\" % score).lstrip(\"0\"),\n size=15,\n horizontalalignment=\"right\",\n )\n i += 1\n\nplt.tight_layout()\nplt.show()\n\nI have found a reference in of the question that you have asked : https://stackoverflow.com/a/74613354/4948889\nThe output of the above code is something like the below:\n\n" ]

[ 1, 1, 1 ]

[]

[]

[ "python", "python_3.x", "scikit_learn" ]

[ "\nwhen we assign an object to a variable(head) we are actually assigning the address of that object to the variable. I would like to know whether my assumption is correct or wrong and if its wrong why is it wrong.\n\nThis would be true for other -- pointer-based -- languages, like C, but Python does not actually give you pointers, not even memory addresses. Python has a layer of abstraction whereby all values are objects, and names can refer to them, and several names can refer to the same object. That's all that is really relevant here, although Python has identifiers for objects (see the id function).\n\ncan someone explain the flow of the code shown in the question.\n\nThe flow can be analysed by using a debugger with which you can step through the code, set breakpoints and inspect values. Here is a rough overview of what happens:\nL = SingleLinkedList()\n\nThis calls the constructor of the SingleLinkedList class: __init__ gets executed, which initialises the attributes of the new object, and L is a name given to that object:\n ┌────────────┐\nL:─┤ head: None │\n └────────────┘\n\nThe next statement n = Node(10) will call the constructor of the Node class, with the data parameter as a name for 10. The new object gets its data and link attributes initialised, and n becomes a name for it:\n ┌────────────┐\nL:─┤ head: None │\n └────────────┘\n\n ┌────────────┐\nn:─┤ data: 10 │\n │ link: None │\n └────────────┘\n\nThe link is made with the assignment: L.head = n\n ┌────────────┐\nL:─┤ head: ─┐ │\n └────────│───┘\n │\n ┌────────┴───┐\nn:─┤ data: 10 │\n │ link: None │\n └────────────┘\n\nSo now both the n name and the L.head attribute reference the same Node object.\nThe next statement, n2 =Node(30) creates another Node object:\n ┌────────────┐\nL:─┤ head: ─┐ │\n └────────│───┘\n │\n ┌────────┴───┐ ┌────────────┐\nn:─┤ data: 10 │ n2:─┤ data: 30 │\n │ link: None │ │ link: None │\n └────────────┘ └────────────┘\n\nAnd n1.link = n2 establishes the link, mutating the n1 object:\n ┌────────────┐\nL:─┤ head: ─┐ │\n └────────│───┘\n │\n ┌────────┴───┐ ┌────────────┐\nn:─┤ data: 10 │ n2:─┤ data: 30 │\n │ link: ────────────┤ link: None │\n └────────────┘ └────────────┘\n\nThe final call of display, will define temp to be what self.head is (with self being L):\n ┌────────────┐\nL:─┤ head: ─┐ │\n └────────│───┘\n temp:─┐ │\n ┌──────┴─┴───┐ ┌────────────┐\nn:─┤ data: 10 │ n2:─┤ data: 30 │\n │ link: ────────────┤ link: None │\n └────────────┘ └────────────┘\n\n..and in the loop temp = temp.link will make temp reference the second node:\n ┌────────────┐\nL:─┤ head: ─┐ │\n └────────│───┘\n │ temp:─┐ \n ┌────────┴───┐ ┌─────┴──────┐\nn:─┤ data: 10 │ n2:─┤ data: 30 │\n │ link: ────────────┤ link: None │\n └────────────┘ └────────────┘\n\nIn a final iteration temp will get the value None, finishing the display call. I hope this clarifies it.\n" ]

[ 1 ]

[ "So linked list has this concept of Start pointer, commonly referred to as Head.\nThe head points to the first node of the linkedlist.\nSo when you say,\nL = SingleLinkedList()\n\nYou essentially create a linked list whose head pointer is still set to None.\nIn the next step you create a node :\nn = Node(10)\n\nAnd then in the next step you make the head pointer of your linked list point to node n1 :\nn = Node(10)\nL.head = n\n\nFurther on you just go on associating n1 to n2 :\nn2 =Node(30)\nn1.link = n2\nL.display()\n\nAt the end you have the LinkedList.\n" ]

[ -1 ]

[ "data_structures", "linked_list", "python", "python_3.x" ]

[ "\nConnectionRefusedError(10061, 'No connection could be made because the target machine actively refused it'\n\nThis has nothing to do with certificates, not even with TLS. This is a connection error at the TCP level, i.e. even before any TLS and certificates are in effect.\n\nBut most websites return the following error ...\n\nIf this is really \"most websites\" then you might have serious problems in your infrastructure which limit access to large parts of the internet. Or, you might need to use a proxy - but ssl.get_server_certificate does not support a proxy.\n" ]

[ 0 ]

[]

[]

[ "certificate", "connection_refused", "python", "ssl" ]

[ "Missing import of class SettingWithSpinner from kivy.uix.settings, related to python settings freezes GUI.\nWithout the import, no errors where reported at runtime and the bug is present, including the import seems to resolve the issue.\n" ]

[ 0 ]

[]

[]

[ "kivy", "python" ]

[ "from_json function for tin in pyspark will get it done.\nExample\nfrom pyspark.sql.functions import from_json, col\nfrom pyspark.sql.types import StructType, StructField, StringType\n\nschema = StructType(\n [\n StructField('col1', StringType(), True),\n StructField('col2', StringType(), True)\n ]\n)\n\ndf.withColumn(\"data\", from_json(\"data\", schema))\\\n .select(col('data.*'))\\\n .show()\n\nand the first column with Array, use explode() function similarly.\n" ]

[ 0 ]

[]

[]

[ "apache_spark_sql", "flatten", "pyspark", "python" ]

[ "Well the error states that os.environ['OMP_NUM_THREADS'] evaluates to '5,3,2' which is a string of multiple numbers. This cannot be converted into an integer.\nCheck out this documentation. So your virtual environment sets different thread numbers for different 'nesting depths'. I think you can set it to a single number yourself with export OMP_NUM_THREADS='5' before you import pandas.\n" ]

[ 0 ]

[]

[]

[ "multithreading", "pandas", "python", "spyder" ]

[ "seems like fixed an issue by importing a specific version of numpy\nimport numpy \nnumpy.__version__ \n'1.21.4' \n\nmake sure, you not importing\nimport numpy as np \n\nafterwards\n", "upgrading this:\npip install -U threadpoolctl\nsolved the prb for me.\n" ]

[ 0, 0 ]

[]

[]

[ "error_handling", "python" ]

[ "Something like this is not achieved by placing that function on your frontend template, what you need to be doing is to redirect the user to a view that contains that function and by extracting these values from the users request, because as you can see you've got methods that are hitting your Database, which isn't something you can achieve from the frontend nor is it safe if it were possible.\n", "From the context I can understand that the arguments that are passed to your function must be secure.\nSo, go with a POST form like this:\ndef get_emails(request):\n context = {\n 'FU_HOST': settings.FU_HOST,\n 'FU_USERNAME': settings.FU_USERNAME,\n 'FU_PASSWORD': settings.FU_PASSWORD,\n 'FV_HOST': settings.FV_HOST,\n 'FV_USERNAME': settings.FV_USERNAME,\n 'FV_PASSWORD': settings.FV_PASSWORD,\n 'USV_HOST': settings.USV_HOST,\n 'USV_USERNAME': settings.USV_USERNAME,\n 'USV_PASSWORD': settings.USV_PASSWORD,\n }\n\n if request.method == \"POST\":\n HOST = request.POST[\"HOST\"]\n USERNAME = request.POST[\"USERNAME\"]\n PASSWORD = request.POST[\"PASSWORD\"]\n\n m = imaplib.IMAP4_SSL(HOST, 993)\n m.login(USERNAME, PASSWORD)\n m.select('INBOX')\n result, data = m.uid('search', None, \"ALL\")\n if result == 'OK':\n for num in data[0].split():\n result, data = m.uid('fetch', num, '(RFC822)')\n if result == 'OK':\n email_message_raw = email.message_from_bytes(data[0][1])\n email_from = str(make_header(decode_header(email_message_raw['From'])))\n email_addr = email_from.replace('<', '>').split('>')\n if len(email_addr) > 1:\n new_entry = EmailMarketing(email_address=email_addr[1], mail_server='X')\n new_entry.save()\n else:\n new_entry = EmailMarketing(email_address=email_addr[0], mail_server='X')\n new_entry.save()\n m.close()\n m.logout()\n\n messages.success(request, f'Subscribers list sychronized successfully.')\n return redirect('subscribers')\n\nIn the template, make a post request, given the host, username and password like this.\n<form action=\"{% url 'name-of-your-view' %}\" method=\"POST\">\n<input type=\"text\" name=\"HOST\">\n<input type=\"text\" name=\"USERNAME\">\n<input type=\"text\" name=\"PASSWORD\">\n<input type=\"submit\">\n</form>\n\n", "For this scenario, if the form post method is not working for you, you can use javascript and ajax.\nAdd the following codes to your html code to use the javascript method.\n<script src=\"https://ajax.googleapis.com/ajax/libs/jquery/3.6.1/jquery.min.js\"></script>\n<script>\n function post(_host,_username,_password){\n $.ajax({\n url: '{% url \"url-name\"%}',\n type: 'POST',\n data: { \n csrfmiddlewaretoken: \"{{ csrf_token }}\",\n host: _host,\n username = _username,\n password = _password\n },\n success: function (res) {\n console.log(res);\n }\n });\n }\n</script>\n\nUpdate the python function as follows.\ndef get_emails(request):\n HOST = request.POST.get('host')\n USERNAME = request.POST.get('username')\n PASSWORD = request.POST.get('password')\n context = {\n 'FU_HOST': settings.FU_HOST,\n 'FU_USERNAME': settings.FU_USERNAME,\n 'FU_PASSWORD': settings.FU_PASSWORD,\n 'FV_HOST': settings.FV_HOST,\n 'FV_USERNAME': settings.FV_USERNAME,\n 'FV_PASSWORD': settings.FV_PASSWORD,\n 'USV_HOST': settings.USV_HOST,\n 'USV_USERNAME': settings.USV_USERNAME,\n 'USV_PASSWORD': settings.USV_PASSWORD,\n }\n m = imaplib.IMAP4_SSL(HOST, 993)\n m.login(USERNAME, PASSWORD)\n m.select('INBOX')\n result, data = m.uid('search', None, \"ALL\")\n if result == 'OK':\n for num in data[0].split():\n result, data = m.uid('fetch', num, '(RFC822)')\n if result == 'OK':\n email_message_raw = email.message_from_bytes(data[0][1])\n email_from = str(make_header(decode_header(email_message_raw['From'])))\n email_addr = email_from.replace('<', '>').split('>')\n if len(email_addr) > 1:\n new_entry = EmailMarketing(email_address=email_addr[1], mail_server='X')\n new_entry.save()\n else:\n new_entry = EmailMarketing(email_address=email_addr[0], mail_server='X')\n new_entry.save()\n m.close()\n m.logout()\n\n messages.success(request, f'Subscribers list sychronized successfully.')\n return redirect('subscribers')\n\nI hope it helps.\n" ]

[ 1, 0, 0 ]

[]

[]

[ "django", "function", "python", "view" ]

[ "Looks like you have a typo in the Redirect URI, in the one being sent to Spotify it is\n\nhttp://127.0.0.1:5000/redirect\n\nNote the spelling of redirect, from the Screenshot from the Spotify Dashboard this is \"redierect\" and also the lack of a forward slash on the one being passed to Spotify as this has to match exactly also so you just need to add that URI to the Spotify Dashboard as is as the URL needs to be exactly correct for it not to show that error for the Invalid Redirect URI so add this one to see if it works\n\nhttp://127.0.0.1:5000/redirect\n\n" ]

[ 0 ]

[]

[]

[ "python", "spotify" ]

[ "Try using Image.quantize() to remove all the unique gray colors before setting the depth/colorspace properties.\n # ...\n image.quantize(2, colorspace_type='gray', dither=True)\n image.depth = 2\n image.colorspace = 'gray'\n image.type = 'bilevel' # or grayscale would also work.\n image.save(filename='result.bmp')\n\nAlso remember filename='bmp3:result.bmp' or filename='bmp2:result.bmp' controls which BMP version to encode with.\n", "For me setting image mode to \"1\" did the job:\nimg.mode='1'\nimg.save('result.bmp')\n\nHere are the docs on different modes: https://pillow.readthedocs.io/en/stable/handbook/concepts.html#concept-modes\n" ]

[ 2, 0 ]

[]

[]

[ "python", "wand" ]

[ "You can try it as below:\n${StartDate}= Convert Date 2022-09-29 result_format=%y%m%d\n\nThis will give your expected output 220929 in yymmdd format\n" ]

[ 0 ]

[]

[]

[ "date", "date_conversion", "python", "robotframework" ]

[ "I am sure this can be solve by different ways. but I believe this is the easiest.\nout = set() # holds the output\nfor ff in f: # loop through f tuple\n ff = set(ff) # convert to set\n for ss1,ss2 in s: # loop through s tuple\n # you can select which tuple to do the intersection on. \n # here I am doing the intersection on both inner tuples in the s tuple.\n ss1 = set(ss1) # convert to set\n ss2 = set(ss2)\n out.update(ff.intersection(ss1)) # intersection and add to out\n out.update(ff.intersection(ss2)) # intersection and add to out\n\n#if you want your output to be in list format\nout = list(out) \n \n\n", "This is an example of how you can proceed\na = ((1,1),(1,2))\nb = (((1,2),(3,1)),((3,2),(1,2)),((1,4),))\nl=[]\nfor t in b:\n c=[i for i in a for j in t if i==j]\n l.append(c)\nprint(l)\n \n\n", "General answer for overall amount of edges:\ndef cnt_edges(a,b):\n edge_cnt = 0\n \n for i in range(len(a)):\n node1 = a[i][0]\n node2 = a[i][1]\n \n for j in range(len(b)):\n inner_node1 = b[j][0]\n inner_node2 = b[j][1]\n \n if (node1 in inner_node1 and node2 in inner_node2) or (node1 in inner_node2 and node2 in inner_node1):\n edge_cnt += 1\n return edge_cnt\n\na = ((0, 1),(0, 2), (0,3))\nb = (((0),(1,2,3)), ((0,1),(2,3)), ((0,1,2),(3)))\n\ncnt_edges(a,b)\n\n" ]

[ 0, 0, 0 ]

[]

[]

[ "python", "tuples" ]

[ "You set the platform equal to Cisco which is not mapped by the netmiko_plugin to any supported device_type. You should read the docs on netmiko_plugin. The platform should be equivalent to cisco_ios or cisco_ios_telnet.\n", "inventory['options']['hosts_query] = replace(replace(replace(replace(platform,'cisco-ios','ios'),'huawei_ce','huawei'),'cisco-nx-os','nxos'),'aruba','aruba_os') AS platform\n" ]

[ 2, 0 ]

[]

[]

[ "error_handling", "netmiko", "python", "python_3.x" ]

[ "Splitting image to tiles of MxN pixels (assuming im is numpy.ndarray):\ntiles = [im[x:x+M,y:y+N] for x in range(0,im.shape[0],M) for y in range(0,im.shape[1],N)]\n\nIn the case you want to split the image to four pieces:\nM = im.shape[0]//2\nN = im.shape[1]//2\n\ntiles[0] holds the upper left tile\n", "Edit: I believe this answer missed the intent to cut an image into rectangles in columns and rows. This answer cuts only into rows. It looks like other answers cut in columns and rows. \nSimpler than all these is to use a wheel someone else invented :) It may be more involved to set up, but then it's a snap to use.\nThese instructions are for Windows 7; they may need to be adapted for other OSs.\nGet and install pip from here.\nDownload the install archive, and extract it to your root Python installation directory. Open a console and type (if I recall correctly):\npython get-pip.py install\n\nThen get and install the image_slicer module via pip, by entering the following command at the console:\npython -m pip install image_slicer\n\nCopy the image you want to slice into the Python root directory, open a python shell (not the \"command line\"), and enter these commands:\nimport image_slicer\nimage_slicer.slice('huge_test_image.png', 14)\n\nThe beauty of this module is that it \n\nIs installed in python \nCan invoke an image split with two lines of code \nAccepts any even number as an image slice parameter (e.g. 14 in this example) \nTakes that parameter and automagically splits the given image into so many slices, and auto-saves the resultant numbered tiles in the same directory, and finally \nHas a function to stitch the image tiles back together (which I haven't yet tested); files apparently must be named after the convention which you will see in the split files after testing the image_slicer.slice function.\n\n", "from PIL import Image\n\ndef crop(path, input, height, width, k, page, area):\n im = Image.open(input)\n imgwidth, imgheight = im.size\n for i in range(0,imgheight,height):\n for j in range(0,imgwidth,width):\n box = (j, i, j+width, i+height)\n a = im.crop(box)\n try:\n o = a.crop(area)\n o.save(os.path.join(path,\"PNG\",\"%s\" % page,\"IMG-%s.png\" % k))\n except:\n pass\n k +=1\n\n", "As an alternative solution, we will construct the tiles by generating a grid of coordinates using itertools.product. We will ignore partial tiles on the edges, only iterating through the cartesian product between the two intervals, i.e. range(0, h-h%d, d) X range(0, w-w%d, d).\nGiven filename: the image file name, d: the tile size, dir_in: the path to the directory containing the image, and dir_out: the directory where tiles will be outputted:\nfrom PIL import Image\nfrom itertools import product\n\ndef tile(filename, dir_in, dir_out, d):\n name, ext = os.path.splitext(filename)\n img = Image.open(os.path.join(dir_in, filename))\n w, h = img.size\n \n grid = product(range(0, h-h%d, d), range(0, w-w%d, d))\n for i, j in grid:\n box = (j, i, j+d, i+d)\n out = os.path.join(dir_out, f'{name}_{i}_{j}{ext}')\n img.crop(box).save(out)\n\n\n", "\ncrop would be a more reusable\nfunction if you separate the\ncropping code from the\nimage saving\ncode. It would also make the call\nsignature simpler.\nim.crop returns a\nImage._ImageCrop instance. Such\ninstances do not have a save method.\nInstead, you must paste the\nImage._ImageCrop instance onto a\nnew Image.Image\nYour ranges do not have the right\nstep sizes. (Why height-2 and not\nheight? for example. Why stop at\nimgheight-(height/2)?).\n\nSo, you might try instead something like this:\nimport Image\nimport os\n\ndef crop(infile,height,width):\n im = Image.open(infile)\n imgwidth, imgheight = im.size\n for i in range(imgheight//height):\n for j in range(imgwidth//width):\n box = (j*width, i*height, (j+1)*width, (i+1)*height)\n yield im.crop(box)\n\nif __name__=='__main__':\n infile=...\n height=...\n width=...\n start_num=...\n for k,piece in enumerate(crop(infile,height,width),start_num):\n img=Image.new('RGB', (height,width), 255)\n img.paste(piece)\n path=os.path.join('/tmp',\"IMG-%s.png\" % k)\n img.save(path)\n\n", "Here is a concise, pure-python solution that works in both python 3 and 2:\nfrom PIL import Image\n\ninfile = '20190206-135938.1273.Easy8thRunnersHopefully.jpg'\nchopsize = 300\n\nimg = Image.open(infile)\nwidth, height = img.size\n\n# Save Chops of original image\nfor x0 in range(0, width, chopsize):\n for y0 in range(0, height, chopsize):\n box = (x0, y0,\n x0+chopsize if x0+chopsize < width else width - 1,\n y0+chopsize if y0+chopsize < height else height - 1)\n print('%s %s' % (infile, box))\n img.crop(box).save('zchop.%s.x%03d.y%03d.jpg' % (infile.replace('.jpg',''), x0, y0))\n\nNotes:\n The crops that go over the right and bottom of the original image are adjusted to the original image limit and contain only the original pixels.\n It's easy to choose a different chopsize for w and h by using two chopsize vars and replacing chopsize as appropriate in the code above.\n", "Not sure if this is the most efficient answer, but it works for me:\nimport os\nimport glob\nfrom PIL import Image\nImage.MAX_IMAGE_PIXELS = None # to avoid image size warning\n\nimgdir = \"/path/to/image/folder\"\n# if you want file of a specific extension (.png):\nfilelist = [f for f in glob.glob(imgdir + \"**/*.png\", recursive=True)]\nsavedir = \"/path/to/image/folder/output\"\n\nstart_pos = start_x, start_y = (0, 0)\ncropped_image_size = w, h = (500, 500)\n\nfor file in filelist:\n img = Image.open(file)\n width, height = img.size\n\n frame_num = 1\n for col_i in range(0, width, w):\n for row_i in range(0, height, h):\n crop = img.crop((col_i, row_i, col_i + w, row_i + h))\n name = os.path.basename(file)\n name = os.path.splitext(name)[0]\n save_to= os.path.join(savedir, name+\"_{:03}.png\")\n crop.save(save_to.format(frame_num))\n frame_num += 1\n\nThis is mostly based on DataScienceGuy answer here\n", "Here is a late answer that works with Python 3\nfrom PIL import Image\nimport os\n\ndef imgcrop(input, xPieces, yPieces):\n filename, file_extension = os.path.splitext(input)\n im = Image.open(input)\n imgwidth, imgheight = im.size\n height = imgheight // yPieces\n width = imgwidth // xPieces\n for i in range(0, yPieces):\n for j in range(0, xPieces):\n box = (j * width, i * height, (j + 1) * width, (i + 1) * height)\n a = im.crop(box)\n try:\n a.save(\"images/\" + filename + \"-\" + str(i) + \"-\" + str(j) + file_extension)\n except:\n pass\n\nUsage:\nimgcrop(\"images/testing.jpg\", 5, 5)\n\nThen the images will be cropped into pieces according to the specified X and Y pieces, in my case 5 x 5 = 25 pieces\n", "Here is another solution, just using NumPy built-in np.array_split :\ndef divide_img_blocks(img, n_blocks=(5, 5)):\n horizontal = np.array_split(img, n_blocks[0])\n splitted_img = [np.array_split(block, n_blocks[1], axis=1) for block in horizontal]\n return np.asarray(splitted_img, dtype=np.ndarray).reshape(n_blocks)\n\nIt returns a NumPy array with the dimension passed as n_blocks.\nEach element of the array is a block, so to access each block and save it as an image you should write something like the following:\nresult = divide_img_blocks(my_image)\n\nfor i in range(result.shape[0]):\n for j in range(result.shape[1]):\n cv2.imwrite(f\"my_block_{i}_{j}.jpg\", result[i,j])\n\nThis answer is very fast, faster than @Nir answer, which among the posted ones was the cleanest. Additionally is almost three orders of magnitude faster than the suggested package (i.e. image_slicer).\nTime taken by divide_img_blocks: 0.0009832382202148438\nTime taken by Nir answer: 0.002960681915283203\nTime taken by image_slicer.slice: 0.4419238567352295\n\nHope it can still be useful.\n", "I find it easier to skimage.util.view_as_windows or `skimage.util.view_as_blocks which also allows you to configure the step\nhttp://scikit-image.org/docs/dev/api/skimage.util.html?highlight=view_as_windows#skimage.util.view_as_windows\n", "import os\nimport sys\nfrom PIL import Image\n\nsavedir = r\"E:\\new_mission _data\\test\"\nfilename = r\"E:\\new_mission _data\\test\\testing1.png\"\nimg = Image.open(filename)\nwidth, height = img.size\nstart_pos = start_x, start_y = (0, 0)\ncropped_image_size = w, h = (1024,1024)\n\nframe_num = 1\nfor col_i in range(0, width, w):\n for row_i in range(0, height, h):\n crop = img.crop((col_i, row_i, col_i + w, row_i + h))\n save_to= os.path.join(savedir, \"testing_{:02}.png\")\n crop.save(save_to.format(frame_num))\n frame_num += 1\n\n", "For anyone looking for a simple approach to this, here is a simple working function for splitting an image into NxN sections.\ndef slice_image(filename, N):\n\n i = Image.open(filename)\n\n width = i.width\n height = i.height\n\n for x in range(N):\n\n for y in range(N):\n\n index = (x * pieces) + 1 + y\n\n img = i.crop((x * width/N, y * height/N,\n x * width/N+ width/N, y * height/N+ height/N))\n\n img.save(f\"{filename}_sliced_{index}.jpeg\")\n\n", "Thanks @Ivan for teaching me something about itertools and grids. Came here to split up tomographic 3D image data (tif-files) into smaller regions for evaluation. I adapted the script to 3D-TIF files (using the tiffile library) and added a \"centered\" approach. So the tiles don't start in the upper-left corner but are centered and crop too small tiles at the borders at each direction. Maybe this also help other people.\nfrom itertools import product\nimport tifffile as tif\nimport numpy as np\n\npath = 'PATH'\nfilename= 'FILENAME.tif'\nimg = tif.imread(path+filename)\n\ndepth, height, width = img.shape\ntilesize = 100\n\ngrid = product(range(int((depth%tilesize)/2), int(depth-(depth%tilesize)/2), tilesize),\n range(int((width%tilesize)/2), int(width-((width%tilesize)/2)), tilesize), \n range(int((height%tilesize)/2), int(height-(height%tilesize)/2), tilesize))\n\nfor z,y,x in grid:\n crop = img[z:z+tilesize, y:y+tilesize, x:x+tilesize]\n tif.imwrite(path+filename+f'{z:04d}z_{y:04d}y_{x:04d}x.tif', crop, dtype = np.uint8)\n\n", "This is my script tools, it is very sample to splite css-sprit image into icons:\nUsage: split_icons.py img dst_path width height\nExample: python split_icons.py icon-48.png gtliu 48 48\n\nSave code into split_icons.py :\n#!/usr/bin/env python\n# -*- coding:utf-8 -*-\nimport os\nimport sys\nimport glob\nfrom PIL import Image\n\ndef Usage():\n print '%s img dst_path width height' % (sys.argv[0])\n sys.exit(1)\n\nif len(sys.argv) != 5:\n Usage()\n\nsrc_img = sys.argv[1]\ndst_path = sys.argv[2]\n\nif not os.path.exists(sys.argv[2]) or not os.path.isfile(sys.argv[1]):\n print 'Not exists', sys.argv[2], sys.argv[1]\n sys.exit(1)\n\nw, h = int(sys.argv[3]), int(sys.argv[4])\nim = Image.open(src_img)\nim_w, im_h = im.size\nprint 'Image width:%d height:%d will split into (%d %d) ' % (im_w, im_h, w, h)\nw_num, h_num = int(im_w/w), int(im_h/h)\n\nfor wi in range(0, w_num):\n for hi in range(0, h_num):\n box = (wi*w, hi*h, (wi+1)*w, (hi+1)*h)\n piece = im.crop(box)\n tmp_img = Image.new('L', (w, h), 255)\n tmp_img.paste(piece)\n img_path = os.path.join(dst_path, \"%d_%d.png\" % (wi, hi))\n tmp_img.save(img_path)\n\n", "I tried the solutions above, but sometimes you just gotta do it yourself.\nMight be off by a pixel in some cases but works fine in general.\nimport matplotlib.pyplot as plt\nimport numpy as np\ndef image_to_tiles(im, number_of_tiles = 4, plot=False):\n \"\"\"\n Function that splits SINGLE channel images into tiles\n :param im: image: single channel image (NxN matrix)\n :param number_of_tiles: squared number\n :param plot:\n :return tiles:\n \"\"\"\n n_slices = np.sqrt(number_of_tiles)\n assert int(n_slices + 0.5) ** 2 == number_of_tiles, \"Number of tiles is not a perfect square\"\n\n n_slices = n_slices.astype(np.int)\n [w, h] = cropped_npy.shape\n\n r = np.linspace(0, w, n_slices+1)\n r_tuples = [(np.int(r[i]), np.int(r[i+1])) for i in range(0, len(r)-1)]\n q = np.linspace(0, h, n_slices+1)\n q_tuples = [(np.int(q[i]), np.int(q[i+1])) for i in range(0, len(q)-1)]\n\n tiles = []\n for row in range(n_slices):\n for column in range(n_slices):\n [x1, y1, x2, y2] = *r_tuples[row], *q_tuples[column] \n tiles.append(im[x1:y1, x2:y2])\n\n if plot:\n fig, axes = plt.subplots(n_slices, n_slices, figsize=(10,10))\n c = 0\n for row in range(n_slices):\n for column in range(n_slices):\n axes[row,column].imshow(tiles[c])\n axes[row,column].axis('off')\n c+=1\n\n return tiles\n\nHope it helps.\n", "I would suggest to use multiprocessing instead of a regular for loop as follows:\nfrom PIL import Image\nimport os\n\ndef crop(infile,height,width):\n im = Image.open(infile)\n imgwidth, imgheight = im.size\n for i in range(imgheight//height):\n for j in range(imgwidth//width):\n box = (j*width, i*height, (j+1)*width, (i+1)*height)\n yield im.crop(box)\n\ndef til_image(infile):\n infile=...\n height=...\n width=...\n start_num=...\n for k,piece in enumerate(crop(infile,height,width),start_num):\n img=Image.new('RGB', (height,width), 255)\n img.paste(piece)\n path=os.path.join('/tmp',\"IMG-%s.png\" % k)\n img.save(path)\n\nfrom multiprocessing import Pool, cpu_count\ntry:\n pool = Pool(cpu_count())\n pool.imap_unordered(tile_image, os.listdir(root), chunksize=4)\nfinally:\n pool.close()\n\n", "the easiest way:\nimport image_slicer\nimage_slicer.slice('/Address of image for exp/A1.png',16)\n\nthis command splits the image into 16 slices and saves them in the directory that the input image is there.\nyou should first install image_slicer:\npip install image_slicer\n\n", "Splitting an image into squares of a specific size\nI adapted a solution so that it accepts a specific tile size instead of an amount of tiles because I needed to cut the image up into a grid of 32px squares.\nThe parameters are the image_path and the size of the tiles in pixels.\nI tried to make the code as readable as possible.\n# Imports\nfrom PIL import Image\nimport os\nimport random\n\n# Function\ndef image_to_tiles(im, tile_size = 32):\n \"\"\"\n Function that splits an image into tiles\n :param im: image: image path\n :param tile_size: width in pixels of a tile\n :return tiles:\n \"\"\"\n image = Image.open(im)\n \n w = image.width\n h = image.height\n \n row_count = np.int64((h-h%tile_size)/tile_size)\n col_count = np.int64((w-w%tile_size)/tile_size)\n \n n_slices = np.int64(row_count*col_count)\n \n # Image info\n print(f'Image: {im}')\n print(f'Dimensions: w:{w} h:{h}')\n print(f'Tile count: {n_slices}')\n\n\n r = np.linspace(0, w, row_count+1)\n r_tuples = [(np.int64(r[i]), np.int64(r[i])+tile_size) for i in range(0, len(r)-1)]\n q = np.linspace(0, h, col_count+1)\n q_tuples = [(np.int64(q[i]), np.int64(q[i])+tile_size) for i in range(0, len(q)-1)]\n \n #print(f'r_tuples:{r_tuples}\\n\\nq_tuples:{q_tuples}\\n')\n \n tiles = []\n for row in range(row_count):\n for column in range(col_count):\n [y1, y2, x1, x2] = *r_tuples[row], *q_tuples[column]\n x2 = x1+tile_size\n y2 = y1+tile_size\n tile_image = image.crop((x1,y1,x2,y2))\n tile_coords = {'x1':x1,'y1':y1,'x2':x2,'y2':y2}\n tiles.append({'image':tile_image,'coords':tile_coords})\n\n return tiles\n\n# Testing:\nimg_path ='/home/user/path/to/image.jpg'\ntiles = image_to_tiles(img_path)\n\nfor i in range(20):\n tile = random.choice(tiles)\n tile['image'].show()\n\n", " '''def split(img,nbxsplit,nbysplit):#this is for number of splitting in line and column\n xpart=int(img.shape[0]/nbxsplit)#number of part or region in line shape of image\n ypart=int(img.shape[1]/nbysplit)#number of part in column shape of image\n arr=[]#empty arr for storage\n for i in range(0,img.shape[0]-xpart+1,xpart):#boucle stepping by part of x and end until she arrived the last part ,in the there will be lost of of little size beacause sometimes thelast part we can't devide it into flly part\n\n for j in range(0,img.shape[1]-ypart+1,ypart):\n arr.append(img[i:i+xpart,j:j+ypart])\n a=np.array(arr)\n a=np.reshape(a,(nbxsplit,nbysplit))\n return a\n '''\n\n", "you can use numpy stride tricks to achive this, but be careful, as this function has to be used with extreme care (doc)\nimport numpy as np\nfrom numpy.lib.stride_tricks import as_strided\n\ndef img_pieces(img, piece_size):\n height, width, chanels = img.shape\n n_bytes = img.strides[-1]\n\n return np.reshape(\n as_strided(\n img,\n (\n height // piece_size,\n width // piece_size,\n piece_size,\n piece_size,\n chanels\n ),\n (\n n_bytes * chanels * width * piece_size,\n n_bytes * chanels * piece_size,\n n_bytes * chanels * width,\n n_bytes * chanels,\n n_bytes\n )\n ),\n (\n -1,\n piece_size,\n piece_size,\n chanels\n )\n )\n\n" ]

[ 44, 41, 39, 34, 20, 4, 3, 3, 3, 2, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0 ]

[ "import cv2\n\ndef crop_image(image_path, output_path):\n im = cv2.imread(os.listdir()[2])\n imgheight=im.shape[0]\n imgwidth=im.shape[1]\n\n y1 = 0\n M = 2000\n N = 2000\n for y in range(0,imgheight,M):\n for x in range(0, imgwidth, N):\n y1 = y + M\n x1 = x + N\n tiles = im[y:y+M,x:x+N]\n if tiles.shape[0] < 100 or tiles.shape[1]<100:\n continue\n\n cv2.rectangle(im, (x, y), (x1, y1), (0, 255, 0))\n cv2.imwrite(output_path + str(x) + '_' + str(y)+\"{}.png\".format(image_path),tiles)\ncrop_image(os.listdir()[2], './cutted/')\n\n" ]

[ -1 ]

[ "crop", "image", "python", "python_imaging_library", "split" ]

[ "The tutorial employs imblearn.pipeline.Pipeline, while your code uses sklearn.pipeline.Pipeline (check import expressions). These appear to be different kinds of pipelines.\n" ]

[ 1 ]

[]

[]

[ "imblearn", "python" ]

[ "You can do this on the Snowflake side if you want by sending the string format as-is and converting to a timestamp_ntz. This single line shows two ways, one that simply strips off the time zone information, and one that converts the time zone to UTC before stripping off the time zone.\nselect try_to_timestamp_ntz('2017-01-01T20:19:47.922596536+09',\n 'YYYY-MM-DD\"T\"HH:MI:SS.FF9TZH') TS_NTZ\n ,convert_timezone('UTC', \n try_to_timestamp_tz('2017-01-01T20:19:47.922596536+09',\n 'YYYY-MM-DD\"T\"HH:MI:SS.FF9TZH'))::timestamp_ntz UTC_TS_NTZ\n;\n\nNote that Snowflake UI by default only shows 3 decimal places (milliseconds) unless you specify higher precision for the output display using to_varchar() and a timestamp format string.\n\n\n\n\nTS_NTZ\nUTC_TS\n\n\n\n\n2017-01-01 20:19:47.922596536\n2017-01-01 11:19:47.922596536\n\n\n\n" ]

[ 0 ]

[]

[]

[ "datetime", "numpy", "pandas", "python", "snowflake_cloud_data_platform" ]

[ "for the existing df you can just add the prediction results like:\nx_train['preds'] = y_predictions\nsame goes for labels like\nx_train['labels'] = y_train\nor all in one:\nnew_df = x_train.copy()\n\nnew_df['preds'] = y_predictions\nnew_df['labels'] = y_train\n\n\nhave you tried this?\n" ]

[ 0 ]

[]

[]

[ "concatenation", "nlp", "pandas", "python", "scikit_learn" ]

[ "Edit: This answer is based on the following version of the question: https://stackoverflow.com/revisions/74613826/2\nYou can use the forest_search.predict(X_test) method, which will use the best parameters found in search.\n", "Or you can try to go to unsupervised learning direction and try one of the clustering methods (for example, KMeans).\nHere is the example:\nfrom sklearn.cluster import KMeans\nimport numpy as np\nX = np.array([[1, 2], [1, 4], [1, 0],\n [10, 2], [10, 4], [10, 0]])\nkmeans = KMeans(n_clusters=2, random_state=0).fit_predict(X)\nprint(kmeans)\n\nCool thing about this approach, that you compare new labels, received after clusting with the results of your RandomForest previous model\n" ]

[ 1, 0 ]

[]

[]

[ "classification", "python", "random_forest", "scikit_learn" ]

[ "I had the same problem, for me the solution was to fully unintall all your versions of python, NOT your code editors, but the Python files.\nUsually located in:\nC:\\Users\\YOUR PC NAME\\AppData\\Local\\Programs\\Python\\Python311\nAfter that go to the official python website:\nhttps://www.python.org/downloads/\nAnd download your prefferred version.\nIn the install click: Add Python... to Path:\nhttps://i.stack.imgur.com/YXYmE.png\nThen install your modules using PIP.\nPlease let me know if this helped!\n\nLexxnl\n\n" ]

[ 0 ]

[]

[]

[ "kivy_language", "pyinstaller", "python" ]

[ "Try specifying endpoint_url in S3 client:\nboto3.client('s3', endpoint_url='https://s3.me-south-1.amazonaws.com', region_name='me-south-1')\n\nIf you get the following error\nThe authorization mechanism you have provided is not supported. Please use AWS4-HMAC-SHA256.\n\nspecify signature_version too:\nfrom botocore.client import Config\nboto3.client('s3', config=Config(signature_version='s3v4'), endpoint_url='https://s3.me-south-1.amazonaws.com', region_name='me-south-1')\n\nI have tried and it works for me ◡̈ \n", "I have the same issue in af-south-1 and this is workaround, which works for me:\ns3 = boto3.client('s3', region_name='af-south-1')\nendpointUrl = s3.meta.endpoint_url\ns3 = boto3.client('s3', endpoint_url=endpointUrl, region_name='af-south-1')\n\nYes, as you can see, boto3 S3 client object knows the proper URL but does not use it.\nOf course it will be better to fix it in boto3.\n" ]

[ 5, 0 ]

[]

[]

[ "amazon_s3", "amazon_web_services", "python" ]

[ "Discord.py 2.0 has built in buttons with discord.ui.buttons use it instead.\nRead the documentation here. There are also plenty of tutorials on it.\n" ]

[ 0 ]

[]

[]

[ "components", "discord", "discord.py", "discord_buttons", "python" ]

[ "Well I solved the problem by completely uninstalling python and reinstalling it\n" ]

[ 0 ]

[]

[]

[ "python", "python_3.x", "scipy" ]

[ "tostring() returns a bytes object unless encoding=\"unicode\" is used.\nThe code can be simplified quite a bit. There is no need to use open(), fromstring() or tostring(). Just parse the XML file into an ElementTree object, do your changes, and save using ElementTree.write().\nfrom xml.etree import ElementTree as ET\n\ntree = ET.parse(\"prices.xml\")\nroot = tree.getroot()\n\nfor price_table in root.findall('price-table'):\n amount = price_table.find('amount')\n if float(amount.text) != 0:\n root.remove(price_table)\n\ntree.write('xmlwrite.txt')\n\n", "If you print the type(xmltowrite) you will see it's a <class 'bytes'>. You can decode it with ET.tostring(root).decode(\"Utf-8\"), than you get <class 'str'>.\n" ]

[ 2, 1 ]

[]

[]

[ "elementtree", "python", "xml" ]

[ "So in summary:\n\npip install -e . makes your installed (live) package Editable, by pointing and using the local (package project) directory for all it's files. This way you can edit the package files and see the results immediately.\npip wheel -e . ignores the -e flag because it only builds the wheel, (in your local directory) and does not install the package. Therefore the -e is redundant.\n\nPS. I am not 100% on the ignoring part, but I cannot see any difference from not using it. It could have additional functionalities not documented.\n" ]

[ 0 ]

[]

[]

[ "pip", "python", "python_3.x", "setuptools" ]

[ "Building cvxopt from source with pip install --no-binary cvxopt cvxopt solved this problem for me.\n" ]

[ 0 ]

[]

[]

[ "cvxopt", "macos", "python" ]

[ "This worked for me, but I don't know what type of response you are expercting. I got no errors and some values in a test as a result.\nimport requests\n\nfiles = [\n ('data', ('file', open('your path to .csv file', 'rb'), 'application/octet-stream'))\n]\n\nresponse = requests.post(\"https://api-adresse.data.gouv.fr/search/csv/\", files=files, params={'q': '8 bd du port', 'limit': 5})\n\nprint(response.text)\n\n\nThis is a POST request, you used a GET request, so use requests.post and not requests.get.\n" ]

[ 0 ]

[]

[]

[ "api", "curl", "python", "python_requests" ]

[ "Not sure if this is the best way to go but, option 2 works with a little modification\nimport pandas as pd\n\nd = {'col1': ['TEST', 'TEST'], 'col2': [[1, 2], [1, 2]], 'col3': [35, 89]}\ndf = pd.DataFrame(data=d)\ndf[\"col2\"] = df[\"col2\"].apply(lambda x: x + [0,0])\nprint(df)\n\nFirstly, if you want to add all members of an iterable to a list use .extend instead of .append. This doesn't work because the method works inplace and doesn't return anything so \"col2\" values become None, so use list summation instead. Finally, you want to assign your modified column to the original DataFrame, not override it (this is the reason for the Series return)\n", "One idea is use list comprehension:\ndf[\"col2\"] = [x + [0,0] for x in df[\"col2\"]]\n \nprint (df)\n col1 col2 col3\n0 TEST [1, 2, 0, 0] 35\n1 TEST [1, 2, 0, 0] 89\n\n" ]

[ 3, 0 ]

[ "have you tried the flowing code?\nfor val in df['col2']:\n val.append(0)\n\nBest Regards,\nStan\n" ]

[ -1 ]

[ "dataframe", "pandas", "python" ]

[ "The .exe crashed because one file is not showing. Such file was belonging to the \"packageofinterest\" (mne)\n\nhmm.. you could try use --collect-data packageofinterest as it seems you are missing some files which are belongs to the package.\nAlso you can use --hidden-import packageofinterest if you don't want to use the spec file.\n", "This question seems old but I hope it helps someone in the future. I recently tried to create a GUI Bundling app. that circumvents as many imported module related issues when I encountered similar interest of forcing PyInstaller to pickup module used by any project I try to Bundle into executable. Here's my workaround.\nEvery Python module has the file and/or path property/ies. This is because every third-party implementation we install for use (via pip, easy_install, etc) is provided as a module(path via file) or a package(path via path). Only those available as a package have access to both file and path properties.\nSo, let's assume the modules that couldn't be picked up by PyInstaller are zipfile and psutil. The former is a module while the later is a package.\nimport zipfile # Use any of the import rules.\nprint( zipfile.__file__ ) # Take a look at where its absolute path resides.\n\nimport psutil\nprint( psutil.__path__ )\nprint( psutil.__file__ ) # This will return the package's __init__.py file. Definitely not what you want.\n\nNow, add this path to your PyInstaller's command using the --add-data option.\nsyntax:\npyinstaller --add-data module_absolute_path;destination my_program_startup_file.py\n\nNOTE:\n-The spaces around the print statements is only for readability sake.\n-The destination is always a '.' for modules and module name for packages Ex: psutil/ [refer PyInstaller documentation for clarity]\nExample:\npyinstaller --onefile --clean --add-data C:\\Users\\USER\\AppData\\Local\\Programs\\Python\\Python310\\Lib\\site-packages\\psutil;psutil\\ my_program_startup_file.py\n\n" ]

[ 0, 0 ]

[]

[]

[ "pyinstaller", "python" ]

[ "You can use cumsum and shift in groupby.apply for the first column, then divide by groupby.cumcount for the second column:\nm = df['show-in-appointment'].eq('no-show')\n\ng = m.groupby(df['name'].str.casefold(), group_keys=False)\ndf['previous-missed-appointments'] = (\n g.apply(lambda x: x.cumsum().shift(fill_value=0))\n )\n\ndf['proportion-previous-missed'] = (\n df['previous-missed-appointments'].div(g.cumcount()).fillna(0)\n)\n\nprint(df)\n\nOutput:\n name day show-in-appointment previous-missed-appointments proportion-previous-missed\n0 Jack 2020/01/01 show 0 0.000000\n1 Jack 2020/01/02 no-show 0 0.000000\n2 Jill 2020/01/02 no-show 0 0.000000\n3 Jack 2020/01/03 show 1 0.500000\n4 Jill 2020/01/03 show 1 1.000000\n5 Jill 2020/01/04 no-show 1 0.500000\n6 Jack 2020/01/04 show 1 0.333333\n7 Jill 2020/01/05 show 2 0.666667\n8 jack 2020/01/06 no-show 1 0.250000\n9 jack 2020/01/07 show 2 0.400000\n\n" ]

[ 1 ]

[]

[]

[ "cumulative_sum", "group_by", "pandas", "proportions", "python" ]

[ "If you need to copy the contents of just one docx file to another, you can use this\nfrom docx import Document\nfrom docxcompose.composer import Composer\n\n# main docx file\nmaster = Document(r\"path\\of\\main.docx\")\ncomposer = Composer(master)\n# doc1 is the docx file getting copied\ndoc1 = Document(r\"file\\to\\be\\copied.docx\")\ncomposer.append(doc1)\ncomposer.save(r\"path\\of\\combined.docx\")\n\nIf you have multiple docx files to be copied, you can try like this\ndef copy_docx(main_docx, docx_list):\n master = Document(main_docx)\n composer = Composer(master)\n for index, file in enumerate(docx_list):\n file = Document(file)\n composer.append(file)\n composer.save(r\"path\\of\\combined.docx\")\n\n\nmain_docx = \"path\\of\\main.docx\"\ndocx_list = [r\"path\\of\\docx1.docx\",\n r\"path\\of\\docx2.docx\",\n r\"path\\of\\docx3.docx\"]\n\ncopy_docx(main_docx, docx_list)\n\n" ]

[ 0 ]

[]

[]

[ "docx", "file_handling", "python", "python_docx" ]

[ "You need to declare your variable (option) as global - and yes, it's very strange. Unfortunately, I could not find the reason.\n", "The reason is that the variable option gets garbage-collected.\nYou can avoid this by having a reference to the variable, e.g., by adding self.option = option.\nThis seems to be similar to the issue of adding images in tkinter, see, e.g., this and this.\n" ]

[ 1, 0 ]

[]

[]

[ "python", "radio_button", "tkinter" ]

[ "What you are looking for may be the new typing.TypeVarTuple as proposed by PEP 646. Due to how new it is (Python 3.11+) and how big of a change this produces, many static type checkers still do not fully support it (see this mypy issue for example).\nMaybe typing.Unpack is actually more applicable in this case, but again hardly useful so long as type checkers don't support it.\nBut at a certain point, you should probably ask yourself, if your design is all that good, if your type annotations become this complex.\n" ]

[ 2 ]

[]

[]

[ "python", "type_hinting" ]

[ "Could you use something like this?\nimport pandas as pd\nimport math\n\n# create a date range\ndr = pd.date_range(\n start=\"2022-02-01\",\n end=\"2022-02-28\",\n freq=\"D\"\n)\n\n# create a dataframe\ndf = pd.DataFrame(\n {\n \"date\": dr\n }\n)\n\n# define a function to get the week number\ndef get_week_in_month(df, date_column):\n df[\"day\"] = df[date_column].dt.day\n\n df[\"week\"] = df[\"day\"].apply(lambda x: math.ceil(x / 7))\n\n del df[\"day\"]\n\n return df\n\n# transform the dataframe\ndf = get_week_in_month(df, \"date\")\n\nThis gives me the following output:\n date week\n0 2022-02-01 1\n1 2022-02-02 1\n2 2022-02-03 1\n3 2022-02-04 1\n4 2022-02-05 1\n5 2022-02-06 1\n6 2022-02-07 1\n7 2022-02-08 2\n8 2022-02-09 2\n9 2022-02-10 2\n10 2022-02-11 2\n11 2022-02-12 2\n12 2022-02-13 2\n13 2022-02-14 2\n14 2022-02-15 3\n15 2022-02-16 3\n16 2022-02-17 3\n17 2022-02-18 3\n18 2022-02-19 3\n19 2022-02-20 3\n20 2022-02-21 3\n21 2022-02-22 4\n22 2022-02-23 4\n23 2022-02-24 4\n24 2022-02-25 4\n25 2022-02-26 4\n26 2022-02-27 4\n27 2022-02-28 4\n\nYou could then format the week number as you needed.\nYou could also do the whole thing in one line using the code below:\ndf[\"week\"] = df[\"date\"].dt.day.apply(lambda x: math.ceil(x / 7))\n\n", "Use:\ndf['date'] = pd.to_datetime(df['date'])\n\ndf['new2'] = ((df[\"date\"].dt.day - 1) // 7 + 1).astype(str) + ' week'\nprint (df)\n date other_columns new2\n0 2022-02-06 row 1 week\n1 2022-02-07 row 1 week\n2 2022-02-08 row 2 week\n3 2022-02-15 row 3 week\n4 2022-02-24 row 4 week\n5 2022-02-28 row 4 week\n\n" ]

[ 2, 1 ]

[]

[]

[ "dataframe", "datetime", "pandas", "python" ]

[ "I think you are using two different libraries to access the file\nFor paramiko you can use the following to get the file\nsftp = ssh.open_sftp()\nsftp.get(remotepath='/mnt/users/username/file.txt', localpath='file.txt')\n\nrather than\nscp = SCPClient(ssh.get_transport())\nscp.get(remote_path='/mnt/users/username/file.txt', local_path='./file.txt', preserve_times=False)\n\n" ]

[ 0 ]

[]

[]

[ "paramiko", "python", "scp" ]

[ "you can check if the following code works!\ndf = pd.DataFrame()\ndf['authors'] = pd.json_normalize(j)['author.display_name']\ndf['institutions'] = pd.json_normalize(j, record_path=['institutions'])['display_name']\ndf\n\n" ]

[ 1 ]

[]

[]

[ "json", "pandas", "python" ]

[ "Remember to put quotes around identifiers that contain upper-case letters:\ncurs.execute('DROP DATABASE \"{}\";'.format(dbName))\n\nNote that string substitution into SQL-statements is generally a bad idea beause it is vulnerable to SQL-injection.\n" ]

[ 1 ]

[]

[]

[ "postgresql", "psycopg2", "python" ]

[ "You're not actually importing the blueprint from routes.py, you're importing the script containing the blueprint hence the error message\n\nAttributeError: module 'routes.routes' has no attribute 'register'\n\nchange this\nfrom routes import routes\nto\nfrom routes.routes import routes\n" ]

[ 0 ]

[]

[]

[ "python" ]

[ "You need to return the tuple inside your function\ndef do_tuple(x: int, y: int, z: int):\n \n tuple_ = (x,y,z)\n summ = x + y + z\n mini = min(tuple_)\n maxi = max(tuple_)\n return (mini, maxi, summ)\n \n \nif __name__ == \"__main__\":\n print(do_tuple(5, 3, -1))\n\n", "As already indicated in a previous answer, you just have to add a return statement in your function. Additionnally, you can use packing to simplify and manage a variable number of arguments. Finally it results in a one-lined and easy-to-read function, as follows:\ndef do_tuple(*args):\n return (max(args), min(args), sum(args))\n \nprint(do_tuple(5, 3, -1)) # (5, -1, 7)\n\n" ]

[ 2, 2 ]

[]

[]

[ "python", "tuples" ]

[ "you have a typo in\nif \"abril.xlsx\" in arquivo():\n\nit should be:\nif \"abril.xlsx\" in arquivo:\n\nWhen you are adding () to the variable name it is trying to \"call\" it - execute as a function, but it is string, that's why you're getting error\n" ]

[ 1 ]

[]

[]

[ "python", "string" ]

[ "It seems to me like the augmented data that's returned from retrieve_and_process_data can (at least in theory) be represented by an asset.\nSo we can start from the standpoint that we'd like to create some asset that takes in country_names_asset, as well as the source data asset (the thing that has a bunch of rows in it, which we can call big_country_data_asset for now). I think this models the underlying relationships a bit better, independent of how we're actually implementing things.\nThe question then is how to write the computation function for this asset in a way that doesn't require loading the entire contents of country_data_asset into memory at any point in time. While it's possible that you could do this with a dynamic graph, which you then wrap in a call to AssetsDefinition.from_graph, I think there's an easier approach.\nDagster allows you to circumvent the IOManager machinery both when reading an asset as input, as well as when writing an asset as output. In essence, when you set an AssetKey as a non_argument_dep, this tells Dagster that there is some asset which is upstream of the asset you're defining, but will be loaded within the body of the asset function (rather than being loaded by Dagster using IOManager machinery).\nSimilarly, if you set the output type of the function to None, this tells Dagster that the asset you're defining will be persisted by the logic inside of the function, rather than by an IOManager.\nUsing both these concepts, we can write an asset which at no point needs to have the entire big_country_data_asset loaded.\n@asset(non_argument_deps={AssetKey(\"big_country_data_asset\")})\ndef processed_country_data_asset(country_names_asset) -> None:\n for name in country_names_asset:\n # assuming this function actually stores data somewhere,\n # and intrinsically knows how to read from big_country_data_asset\n retrieve_and_process_data(name)\n\nIOManagers are a very flexible concept however, and it is possible to replicate all of this same batching behavior while using IOManagers (just a bit more convoluted). You'd need to do something like create a SourceAsset(key=\"big_country_data_asset\", io_manager_def=my_custom_io_manager), where my_custom_io_manager has a weird load_input function which itself returns a function like:\ndef load_input(context):\n def _fn(country_name):\n # however you actually get these rows\n rows = query_source_data_for_name(country_name)\n return rows\n return _fn\n\nthen, you could define your asset like:\n@asset\ndef processed_country_data_asset(\n country_names_asset, big_country_data_asset\n) -> None:\n for name in country_names_asset:\n # big_country_data_asset has been loaded as a function\n rows = big_country_data_asset(name)\n process_data(rows)\n\nYou can also handle writing the output of this function in an IOManager using a similar-looking trick: https://github.com/dagster-io/dagster/discussions/9772\n" ]

[ 1 ]

[]

[]

[ "dagster", "python" ]

[ "Some of your files in the validation folder are not in the format accepted by Tensorflow ( JPEG, PNG, GIF, BMP), or may be corrupted. The extension of a file is indicative only, and does not enforce anything on the content of the file.\nYou might be able to find the culprit using the imghdr module from the python standard library, and a simple loop.\nfrom pathlib import Path\nimport imghdr\n\ndata_dir = \"/home/user/datasets/samples/\"\nimage_extensions = [\".png\", \".jpg\"] # add there all your images file extensions\n\nimg_type_accepted_by_tf = [\"bmp\", \"gif\", \"jpeg\", \"png\"]\nfor filepath in Path(data_dir).rglob(\"*\"):\n if filepath.suffix.lower() in image_extensions:\n img_type = imghdr.what(filepath)\n if img_type is None:\n print(f\"{filepath} is not an image\")\n elif img_type not in img_type_accepted_by_tf:\n print(f\"{filepath} is a {img_type}, not accepted by TensorFlow\")\n\nThis should print out whether you have files that are not images, or that are not what their extension says they are, and not accepted by TF. Then you can either get rid of them or convert them to a format that TensorFlow supports.\n", "this should work fine, the same for supported types ... ex for png :\nimage = tf.io.read_file(\"im.png\")\nimage = tf.image.decode_png(image, channels=3)\n\n", "TensorFlow has some strictness when dealing with image formats. This should guide in deleting the bad images. Some times your data set may even run well with, for instance Torch but will generate a format error with Tf. Nonetheless, it is best practice to always carryout preprocessing on the images to ensure a robust, safe and standard model.\nfrom pathlib import Path\nimport imghdr\n\nfrom pathlib import Path\nimport imghdr\n\nimg_link=list(Path(\"/home/user/datasets/samples/\").glob(r'**/*.jpg'))\n\ncount_num=0\nfor lnk in img_link:\n binary_img=open(lnk,'rb')\n find_img=tf.compat.as_bytes('JFIF') in binary_img.peek(10)#The JFIF is a JPEG File Interchange Format (JFIF). It is a standard which we gauge if an image is corrupt or substandard\n if not find_img:\n count_num+=1\n os.remove(str(lnk))\nprint('Total %d pcs image delete from Dataset' % count_num)\n#this should help you delete the bad encoded\n\n" ]

[ 13, 0, 0 ]

[]

[]

[ "python", "tensorflow" ]

[ "One possible reason to run into this error (as in my case), is that the system reaches a limit of allowed POSIX semaphores. This limit can be inspected by the sysctl kern.posix.sem.max command and is 10000 on my macOS 13.0.1.\nTo set it, for example to 15000 until next reboot, you can use:\nsudo sysctl -w kern.posix.sem.max=15000\n\nWhile this allowed the Python script to run, I wasn't able to find out which processes were actually using up the semaphores. The only way I found of listing this type of semaphores was lsof. It shows them as type PSXSEM, e.g.:\nsudo lsof | grep PSXSEM\n\nBut it found only a couple of the semaphores -- not nearly enough to justify reaching the limit. So, I suspect a bug in the system, where semaphores are not cleaned up correctly. Further evidence to this is that after a reboot the script was able to run with the initial limit set.\n" ]

[ 0 ]

[]

[]

[ "multiprocessing", "oserror", "python", "python_3.x" ]

[ "\nWell, the .lsfunction will list all the file in the folder. Instead of that I will suggest that you download the file and then read the file.\n\nTo this you will need to run the following code.\n\n\nmultithread.ADLDownloader(adlsFileSystemClient,lpath=\"< Local Path >\",rpath=\"<Path to file>\")\n\n\nAlso, while reading the file you need an engine for parquet, we can use pyarrow or we can use fastparquet for this the code will look like this:\n\npandas.read_parquet(\"<path of the file>\",engine=\"pyarrow\")\n\n\nRefer this MSDOC on Datalake gen1 .\n", "I finally manage to solve the problem. The problem was memory consumption. As I am still running the app in a dev enviroment it has just 1.5Gb of memory. I was reading the whole parquet file which was consuming approximatelly 2.5Gb. I changed my code so I use the pyarrow.read_table with filter option and reading just the necessary columns from the parquet file. This reduced the memory consumption and the app function started working.\n" ]

[ 0, 0 ]

[]

[]

[ "azure_functions", "parquet", "python" ]

[]

[]

[ "the model needs to be initialized you can do the model.compiled() which is a good step to do only sometimes Tensorflow loads weights without initializing the values or you can use model.fit() which will prevent the errors.\nFor my examples, the custom layer tells you about how the weights are generated inside the layer, and that needs to match with the optimizer and parameters of the working programs.\nFor machines works or build robots they use small tools to understand only the number and matrixes capable, no Tensorflow installed that included third-party applications, and next web platforms implementing they build Java versions but that is why I don't install it over communications devices that they copied and reverts.\nSample: Weights initialized with shape and dtypes ( across versions may support different dtypes )\n\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n: Class\n\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\nclass MyLSTMLayer( tf.keras.layers.LSTM ):\n def __init__(self, units, return_sequences, return_state):\n super(MyLSTMLayer, self).__init__( units, return_sequences=return_sequences, return_state=return_state )\n self.num_units = units\n self.return_sequences = return_sequences\n self.return_state = return_state\n\n def build(self, input_shape):\n self.w = self.add_weight(\n shape=(input_shape[-1], self.units),\n initializer=\"random_normal\",\n trainable=True,\n )\n self.b = self.add_weight(\n shape=(self.units,), initializer=\"random_normal\", trainable=True\n )\n\n def call(self, inputs):\n return tf.matmul(inputs, self.w) + self.b \n\nSample: Final products running on machines\ntemp = tf.random.normal([10], 1, 0.2, tf.float32)\ntemp = np.asarray(temp) * np.asarray([ coefficient_0, coefficient_1, coefficient_2, coefficient_3, coefficient_4, coefficient_5, coefficient_6, coefficient_7, coefficient_8, coefficient_9 ]) #action = actions['up']\ntemp = tf.nn.softmax(temp)\naction = int(np.argmax(temp))\n\nThe values in matrixes is game variances, select max() or min() mapped to target actions { UP, UP, UP, UP, UP, HOLD, HOLD, HOLD, HOLD, HOLD }\n\n" ]

[ -1 ]

[ "keras", "python", "tensorflow" ]

[ "After speaking with the sitemap hosting provider (pro-sitemaps.net) it appears that the problem was on their end. The HTML and TXT files I was downloading were being served with the wrong encoding (or something similar to that). Though these files were visible/accessible in the browser from the direct URLs at their service, they were not being served via wget with the right encoding it appears.\nI submitted a ticket to the provider and the issue was resolved within 12 hours (though I didn't get confirmation of the exact issue that caused my problem here). I have now verified that the TXT and HTML files are being served by them in the correct encoding/format via wget.\n" ]

[ 0 ]

[]

[]

[ "django", "python", "python_3.x", "urllib", "wget" ]

[ "no loop is needed , not familiar with yf but nevertheless you can use apply :\ndf['price'] = df.apply(lambda x : yf.download(x.['ticker'], x.['start_date'], x.['start_date']))\n\n" ]

[ 0 ]

[]

[]

[ "pandas", "python", "stock", "yfinance" ]

[ "The error probably comes from multiple Tk instances. Try removing the for-loop and then it will work. But if your intention was for multiple windows, then you can look into this answer: https://stackoverflow.com/a/36316105/9983213. Feel free to tinker around with the example.\nA smaller example is:\nimport tkinter as tk\n\nroot = tk.Tk()\nfor i in range(5):\n top = tk.Toplevel(root)\n\nroot.mainloop()\n\n" ]

[ 2 ]

[]

[]

[ "python", "python_3.x", "tkinter" ]

[ "As I understand you are looking for specific subseries in a series.\nTo illustrate different solutions consider an example\nimport numpy as np\nimport pandas as pd\n\nNVALUES = 10\nNSEGMENT = 3\nISTART = 2\n\ndfb = pd.DataFrame(dict(values=np.random.rand(NVALUES)))\ndfa = pd.DataFrame(dict(values=range(NSEGMENT)))\ndfb[ISTART:ISTART + NSEGMENT] = dfa.values\n\nWe have a dataframe dfb with random values and a segment dfa. And dfb is manipulated so that the NSEGMENT consecutive values in dfb starting from ISTART are copied from dfa.\nNow we need a function that given dfa and dfb would return ISTART.\nHere is a straightforward solution.\nSolution A.\ndef find_sequence(dfa, dfb):\n nsegment = len(dfa) \n for i in range(len(dfb) - nsegment):\n if (dfb[i:i+nsegment] - dfa.values == 0).all().values[0]:\n return i\n\nThis function returns ISTART or None if subseries equal to dfa is not found.\nIn our example find_sequence(dfa, dfb) returns 2.\nThere are several issues with this solution but the general idea should be clear.\nSolution B.\nA vectorized solution is less straightforward but avoids explicit iteration which is considered a good thing.\nTurn the dataframe b into a matrix\nmatrix = dfb\nfor i in range(len(dfa)-1):\n matrix = matrix.join(dfb['values'].shift(-i - 1).rename(i))\n\nNow the matrix would look like a dataframe where each row starts with an element of dfb and continues with consecutive elements of dfb directly following the first element in a row.\nIn our example the matrix might look like this:\n values 0 1\n0 0.271286 0.355040 0.315251\n1 0.355040 0.315251 0.000000\n2 0.315251 0.000000 1.000000\n3 0.000000 1.000000 2.000000\n...\n8 0.305554 0.105416 NaN\n9 0.105416 NaN NaN\n\nNow you can compare the rows to the values from dfa with a single call to apply(). The following would return all the indices of the elements in dfb where the following NSEGMENT elements match the ones in dfa.\ndfb.index[matrix.apply(lambda x: \n (x.values - np.transpose(dfa.values) == 0).all(), axis=1)]\n\nIn our case this would return\nInt64Index([2], dtype='int64')\n\n" ]

[ 0 ]

[]

[]

[ "dataframe", "python" ]

[ "I think this is kind of more pythonic way to do so, because you do not have to worry about indexes:\nimport pandas as pd\n\ndef find_from_index(series: pd.Series, to_find: str) -> pd.Series:\n return pd.Series([v.find(to_find, i) for i, v in enumerate(series)])\n\na = pd.Series(data=['aaba', 'ababc', 'cbaauuab'])\nb = find_from_index(a, 'b')\n\nHope this helps\n", "No there isn't, vectorizing string operations is difficult.\nYou could think to convert your strings to arrays of characters, but the conversion would be the limiting step. A quick test tells me that it is roughly taking the same time than performing the list comprehension provided in your question. And we haven't even searched for the position yet.\nIn short, your current approach seems reasonably the most efficient.\n" ]

[ 0, 0 ]

[]

[]

[ "pandas", "python", "string" ]

[ "You may use localization inside group by. This way your group will contain all the times for 1st day and 23:00 for the 31st day, same as in you first table reference.\ndf.groupby(df.index.tz_localize(\"UTC\").tz_convert(\"Europe/Copenhagen\").day).get_group(1)\n\nBtw, a snippet to reproduce your situation:\nimport pandas as pd\nimport numpy as np\nrng = pd.date_range(\"2022-10-31 23:00:00\", periods=28*24, freq='H')\ndf = pd.DataFrame({'col1':np.random.randn(len(rng))}, index=rng)\n\nUPD:\nAnother solution escaping time zones would be assembling groups based on a function:\ngroup_counter = 0\ndef GroupFunc(ind):\n global group_counter\n if ind.hour == 23:\n group_counter += 1\n return group_counter\ndf.groupby(lambda x: GroupFunc(x))\n\nHere .groupby() will iterate through the whole DataFrame and each time it finds 23:00 it will create a new group. This means each group will be 23 to 22.\n⚠ in this case the original DataFrame should be sorted ascending.\nDoing .groupby() with custom functions has a rare use, is considered a bit difficult and it's difficult to understand and maintain without comments.\nBut! You don't have to care about time zones anymore. And in my case the custom function approach worked 4 times faster. \n" ]

[ 2 ]

[]

[]

[ "datetime", "group_by", "pandas", "python" ]

[ "The following should work:\nfrom matplotlib import pyplot as plt\n\nfig, ax = plt.subplots()\n\ndata = [32, 45, 67, 12, 1]\n\nax.pie(data)\n\n# turn on frame\nax.set_frame_on(b=True)\n\n# create locations of grid points\nxrange = ax.get_xlim()\nngrids = 7\ndx = (xrange[1] - xrange[0]) / ngrids\ngridvals = [xrange[0] + (dx / 2) + i * dx for i in range(ngrids)]\n\n# set grid location\nax.set_yticks(gridvals, minor=False)\nax.set_xticks(gridvals, minor=False)\n\n# set tick labels (to be from 1 to 7)\nax.set_xticklabels([f\"{i}\" for i in range(1, ngrids + 1)])\nax.set_yticklabels([f\"{i}\" for i in range(1, ngrids + 1)])\n\n# turn grid on\nax.yaxis.grid(True, which=\"major\")\nax.xaxis.grid(True, which=\"major\")\n\nTo use the Blues color map, see the example here.\n" ]

[ 1 ]

[]

[]

[ "matplotlib", "numpy", "python" ]

[ "I guess your problem is not with set_page_load_timeout.\nYou need to use WebDriverWait expected_conditions to wait for element to become clickable.\nwait.until(EC.element_to_be_clickable((By.XPATH, xpath)))\n\nSince you did not share link and xpath details I can't give more detailed answer here.\n" ]

[ 0 ]

[]

[]

[ "python", "python_3.x", "selenium", "webdriverwait", "xpath" ]

[ "I faced the same issue a couple of days back. I would suggest you to download Anaconda 2020.02 for a seamlessly smooth experience with TensorFlow 2.\nAfter installation;\nYou can execute the following instructions and command to solve the issue: (do not include inverted commas)\nOpen Anaconda Prompt\ntype: create --name py3-TF2.0 python = 3\npress y to continue After successful completion:\ntype conda activate py3-TF2.0\ntype pip install matplotlib\ntype pip install tensorflow==2.3.0\ntype conda deactivate\ntype pip install ipykernel\ntype conda install nb_conda_kernels\nNow exit the Anaconda prompt and open the Anaconda Navigator application. Switch to py3-TF2.0 from the base environment using the Environment tab. Install Jupyter in the specified environment. After successful installation, open Jupyter notebook and switch to py3-TF2.0 kernel using Kernel Tab. You're good to go for using TensorFlow 2.\n" ]

[ 0 ]

[]

[]

[ "anaconda3", "python", "tensorflow2.0" ]

[ "A better would be to create a dictionary with username as key and value as list of user attributes\nop = defauldict(list)\nfor user_dic in list_of_userdictss:\n op[user_dic.pop('username')].append(user_dic)\nop = OrderedDict(sorted(user_dic.items()))\n\n", "Finding the same thing works the best if we sort the list by it - then all the same names are next to each other.\nBut even after sorting, we don't need to do such things manually - there are already tools for that. :) - itertools.groupby documentation and a nice explanation how it works\nfrom itertools import groupby\nfrom operator import itemgetter\n\nmy_list.sort(key=itemgetter(\"username\"))\nresult = {}\nfor username, group in groupby(my_list, key=itemgetter(\"username\")):\n result[username] = list(group)\n\nresult is a dict with usernames as keys\nIf you want a list-of-lists, do result = [] and then result.append(list(group)) instead.\n", "list of dict to separate lists\ndata = pd.DataFrame(your_list_of_dict)\n\nusername_list = data.username.values.tolist()\n\n" ]

[ 0, 0, 0 ]

[]

[]

[ "dictionary", "if_statement", "list", "loops", "python" ]

[]

[]

[ "I suggest changing the expected behavior of your API.\nHow about having two endpoints:\n\n'/sempos/start' - starts the AI prediction and immediately returns the id of the calculation\n'/sempos/result/{id}' - gives a generic message if calculations aren't done, else gives the calculation results JSON\n\nThis way you can:\n\nLeverage the speed of FastAPI\nUse the flexibility of asyncio\nFit your use case\n\nWhat do you think?\n" ]

[ -1 ]

[ "fastapi", "python", "python_3.x", "python_asyncio" ]

[ "To find the shortest file just compare to the current shortest:\nchosen_file = \"\"\n\nfor file in glob.glob(\"*.txt\"):\n if chosen_file == \"\" or len(file) < len(chosen_file):\n chosen_file = file\n\nprint(f\"{chosen_file} is the shortest file\")\n\n\nOnce you've finished the loop, the chosen_file str is guaranteed to be the shortest.\n", "Cleaner to put it in a function and calling it:\n\nimport glob\nimport os\n\ndef shortest_file_name(inpDir: str, extension: str) -> str:\n os.chdir(inpDir)\n shortest, l = '', 0b100000000\n for file in glob.glob(extension):\n if len(file) < l:\n l = len(file)\n shortest = file\n return shortest\n\ninpDir = \"C:/Users/ft/Desktop/Folder\"\nmin_file_name = shortest_file_name(inpDir, \"*.txt\")\n\n", "min(glob.glob('*.txt'), key=len)\n\n" ]

[ 2, 0, 0 ]

[ "min = 1000\n\nfor file in glob.glob(\"*.txt\"):\n if len(file) < min:\n min = len(file)\n name = file\n\n" ]

[ -3 ]

[ "python" ]

[ "assertTrue(\"x\" in s or \"y\" in s).\n– MrBean Bremen\n" ]

[ 0 ]

[]

[]

[ "python", "python_unittest", "unit_testing" ]

[ "I wouldn't call what you've done hacky for what you are trying to do. Your alternative approach from my understanding is to make use of python-specific tags in your YAML file so it is represented appropriately when loading the yaml file. However, this requires you modifying your yaml file which, if huge, is probably going to be pretty irritating and not ideal.\nLook at the PyYaml doc that further illustrates this. Ultimately you want to place a !!python/tuple in front of your structure that you want to represented as such. To take your sample data, it would like: \nYAML FILE:\ncities:\n 1: !!python/tuple [0,0]\n 2: !!python/tuple [4,0]\n 3: !!python/tuple [0,4]\n 4: !!python/tuple [4,4]\n 5: !!python/tuple [2,2]\n 6: !!python/tuple [6,2]\nhighways:\n - !!python/tuple [1,2]\n - !!python/tuple [1,3]\n - !!python/tuple [1,5]\n - !!python/tuple [2,4]\n - !!python/tuple [3,4]\n - !!python/tuple [5,4]\nstart: 1\nend: 4\n\nSample code: \nimport yaml\n\nwith open('y.yaml') as f:\n d = yaml.load(f.read())\n\nprint(d)\n\nWhich will output: \n{'cities': {1: (0, 0), 2: (4, 0), 3: (0, 4), 4: (4, 4), 5: (2, 2), 6: (6, 2)}, 'start': 1, 'end': 4, 'highways': [(1, 2), (1, 3), (1, 5), (2, 4), (3, 4), (5, 4)]}\n\n", "Depending on where your YAML input comes from your \"hack\" is a good solution, especially if you would use yaml.safe_load() instead of the unsafe yaml.load(). If only the \"leaf\" sequences in your YAML file need to be tuples you can do ¹:\nimport pprint\nimport ruamel.yaml\nfrom ruamel.yaml.constructor import SafeConstructor\n\n\ndef construct_yaml_tuple(self, node):\n seq = self.construct_sequence(node)\n # only make \"leaf sequences\" into tuples, you can add dict \n # and other types as necessary\n if seq and isinstance(seq[0], (list, tuple)):\n return seq\n return tuple(seq)\n\nSafeConstructor.add_constructor(\n u'tag:yaml.org,2002:seq',\n construct_yaml_tuple)\n\nwith open('input.yaml') as fp:\n data = ruamel.yaml.safe_load(fp)\npprint.pprint(data, width=24)\n\nwhich prints:\n{'cities': {1: (0, 0),\n 2: (4, 0),\n 3: (0, 4),\n 4: (4, 4),\n 5: (2, 2),\n 6: (6, 2)},\n 'end': 4,\n 'highways': [(1, 2),\n (1, 3),\n (1, 5),\n (2, 4),\n (3, 4),\n (5, 4)],\n 'start': 1}\n\nif you then need to process more material where sequence need to be \"normal\" lists again, use:\nSafeConstructor.add_constructor(\n u'tag:yaml.org,2002:seq',\n SafeConstructor.construct_yaml_seq)\n\n\n¹ This was done using ruamel.yaml a YAML 1.2 parser, of which I am the author. You should be able to do same with the older PyYAML if you only ever need to support YAML 1.1 and/or cannot upgrade for some reason\n", "I ran in the same problem as the question and I was not too satisfied by the two answers. While browsing around the pyyaml documentation I found\nreally two interesting methods yaml.add_constructor and yaml.add_implicit_resolver. \nThe implicit resolver solves the problem of having to tag all entries with !!python/tuple by matching the strings with a regex. I also wanted to use the tuple syntax, so write tuple: (10,120) instead of writing a list tuple: [10,120] which then gets\nconverted to a tuple, I personally found that very annoying. I also did not want to install an external library. Here is the code:\nimport yaml\nimport re\n\n# this is to convert the string written as a tuple into a python tuple\ndef yml_tuple_constructor(loader, node): \n # this little parse is really just for what I needed, feel free to change it! \n def parse_tup_el(el): \n # try to convert into int or float else keep the string \n if el.isdigit(): \n return int(el) \n try: \n return float(el) \n except ValueError: \n return el \n\n value = loader.construct_scalar(node) \n # remove the ( ) from the string \n tup_elements = value[1:-1].split(',') \n # remove the last element if the tuple was written as (x,b,) \n if tup_elements[-1] == '': \n tup_elements.pop(-1) \n tup = tuple(map(parse_tup_el, tup_elements)) \n return tup \n\n# !tuple is my own tag name, I think you could choose anything you want \nyaml.add_constructor(u'!tuple', yml_tuple_constructor)\n# this is to spot the strings written as tuple in the yaml \nyaml.add_implicit_resolver(u'!tuple', re.compile(r\"\\(([^,\\W]{,},){,}[^,\\W]*\\)\")) \n\nFinally by executing this:\n>>> yml = yaml.load(\"\"\"\n ...: cities:\n ...: 1: (0,0)\n ...: 2: (4,0)\n ...: 3: (0,4)\n ...: 4: (4,4)\n ...: 5: (2,2)\n ...: 6: (6,2)\n ...: highways:\n ...: - (1,2)\n ...: - (1,3)\n ...: - (1,5)\n ...: - (2,4)\n ...: - (3,4)\n ...: - (5,4)\n ...: start: 1\n ...: end: 4\"\"\")\n>>> yml['cities']\n{1: (0, 0), 2: (4, 0), 3: (0, 4), 4: (4, 4), 5: (2, 2), 6: (6, 2)}\n>>> yml['highways']\n[(1, 2), (1, 3), (1, 5), (2, 4), (3, 4), (5, 4)]\n\nThere could be a potential drawback with save_load compared to load which I did not test.\n", "You treat a tuple as a list.\nparams.yaml\nfoo:\n bar: [\"a\", \"b\", \"c\"]\n\nSource\n" ]

[ 31, 5, 4, 0 ]

[]

[]

[ "python", "pyyaml", "yaml" ]

[ "I obviously confused CORS with CORP although they're related.\nThe solution I found was:\[email protected](\"/\")\ndef home():\n return render_template(\"index.html\")\n\[email protected]_request\ndef add_header_home(response):\n response.headers['Cross-Origin-Embedder-Policy'] = 'require-corp'\n response.headers['Cross-Origin-Opener-Policy'] = 'same-origin'\n return response\n\nSomehow after_request decorator helps us here.\n" ]

[ 0 ]

[]

[]

[ "cross_origin_embedder_policy", "cross_origin_resource_policy", "flask", "javascript", "python" ]

[ "You can use a function:\ndef fill_in_capabilities(df: pd.DataFrame) -> pd.DataFrame:\n df[[\"LEO_Capa\", \"GTO_Capa\"]] = df[[\"LEO_Capa\", \"GTO_Capa\"]].fillna(\n df.groupby(\"LV_Name\")[[\"LEO_Capa\", \"GTO_Capa\"]].transform(\"mean\")\n )\n\n return df\n\n\ndf = fill_in_capabilities(df)\n\n" ]

[ 1 ]

[]

[]

[ "dataframe", "nan", "pandas", "python", "series" ]

[ "two options here:\n\nlet form direct to url /user_data, and based on the Period value renders the page i.e it renders the data for that month.\nas value is based on user selection, JS can be utilized.\n\n<html>\n <body>\n<form action = \"/user_data/{{period}}\" method=\"POST\" id=\"myForm\">\n <label for = \"Period\" id = \"Period\">Period</label>\n <input list=\"User_Listing_Period\" id =\"User_Lst_Period\" name = \"Period\">\n <datalist id = \"User_Listing_Period\">\n <!-- I commented this, so can check in HTML, you can go with your code\n {% for row in user_dat_filter %} -->\n <option name = \"User_Dat_Period\" value = \"Aug22\"></option>\n <option name = \"User_Dat_Period\" value = \"Sep22\"></option>\n <option name = \"User_Dat_Period\" value = \"Oct222\"></option>\n <!-- {% endfor %} -->\n </datalist>\n <div class=\"submit\">\n <label for = \"Submit\" id = \"Submit\"></label>\n <button type=\"submit\">Submit</button>\n </div>\n </form>\n <script>\n const myform= document.getElementById(\"myForm\")//identify the form\n // add a listener to myform\n myform.addEventListener('submit', async (e) => {\n e.preventDefault(); //block default submit\n const selectedOption=document.getElementById(\"User_Lst_Period\")\n let value = selectedOption.value\n document.location.href='/user_data/' + value //redirect to value selected by user\n\n\n })\n </script>\n</body>\n</html>\n\n" ]

[ 1 ]

[]

[]

[ "flask", "python" ]

[ "This looks like a pandas dataframe and you would like to change the data type of the column 'Work Ratio'? The warning tells you that by changing df_under['Work Ratio'] you will not change the actual dataframe in place. The warning tells you to access the column by saying\ndf_under.loc[:,'Work Ratio']=df_under['Work Ratio'].astype(float) #all rows, column 'Work Ratio'\n\nAlternatively you could save the data types of your dataframe\ncol_types = df_under.dtypes\n\nwhich gives you a pandas series of types. Now change the type of 'Work Ratio'\ncol_types['Work Ratio'] = float\n\nand change the whole dataframe as\ndf_under = df_under.astype(col_types)\n\n" ]

[ 0 ]

[]

[]

[ "python" ]

[ "This error means that you installed pyqt5 with pip along side the pyqt conda package. It could be solved by you uninstalling the pip package.\nTry:\npip uninstall PyQt5\n\nThen update conda:\nconda update conda\n\nand \nconda update anaconda-navigator\n\nIt will surely resolve your problem.\n", "I tried all the solutions listed here but they didn't work for me. Later I was able to resolve the issue. Even though your solutions didn't directly solve my issue, the steps I used to solve it were based on the answers I found here. I will now list the steps I used to solve my issue:\n\npython -V # I checked the python version to make sure it's python 3.4 and above\nconda update conda\nconda update anaconda-navigator\npip install PySide2\npip uninstall pyqt\npip uninstall PyQt5\n\nNow when doing step 5&6 both pyqt and PyQt5 were not installed on the system, which was the cause of the issue\n\npip install qtpy # This module was already installed on the system\npip install PyQt5 # This was the final step that solved the issue\n\n", "I tried all the answers provided. Some steps worked some didn't. So I will just tell all the steps which finally after many trials and fails worked for me:\n\ncd C:\\Users\\UserName\\Anaconda\\Scripts\npip install PySide2\npip uninstall PyQt5\npip uninstall pyqt (on this I got this error- WARNING: Skipping pyqt as it is not installed. So not sure if this is helpful)\nconda update conda\nconda update\nanaconda-navigator\n\n", "I had exactly this problem. Same error message and all.\nTo fix I first updated Conda:\n$ conda update conda\n\nThen updated the navigator\n$ conda update anaconda-navigator\n\nThis performed an integrity check (though it took a while - you have to be patient) and found that the environment was inconsistent. It created a package plan to download and install new packages and updates as well as downgrade some packages. It asked me to approve the change before performing the change and updating the specs.\nHaving approved it, all was performed flawlessly and I was able to relaunch Anaconda navigator without a problem. \n", "I just had a similar problem with Navigator. I typed anaconda-navigator on the command line and it opened fine. This doesn't explain why it won't open from the icon but does offer a work-around.\n", "Currently I have two anaconda prompt windows open on my screen, one normal, and one that is running as an Admin. \nWhen I try 'anaconda-navigator' in the normal window, I get the same errors as you.\nWhen I try the same command in the admin rights window, then it all works. \nThis isn't really a fix, but it might point someone more experienced in the right direction. \n", "The real fix that worked for me was to install the module that was causing the error, that is the PySide module. Do not install PySide (pip install PySide) unless you are using Python 3.4 or less. If you are running Python 3.4 and up (up to 3.7), you need to install PySide2 (pip install PySide2).\nNone of the other answers worked for me at all because the error was the same all over again.\n", "Uninstalled PyQt5\npip uninstall PyQt5\n\nand installed qtpy\npip install qtpy\n\nworked for me\n", "Run these one by one, it worked for me:\npip uninstall pyqt\npip uninstall PyQt5\nconda update conda\nconda update anaconda-navigator\n\n", "I had a similar problem with Anaconda Navigator and Spyder. This commands on the anaconda prompt worked for me:\nconda update conda\nconda update anaconda-navigator\nconda install pyside2\n\n", "Try this :\nconda update conda\nconda update anaconda-navigator\n\nAnd finally:\npip install PyQt5\n\nIt worked for me fine\n", "Make sure that the path in system environment variables point to the right folder as shown in image.\nC:\\Anaconda3\\Scripts,\nC:\\Anaconda3\n\n\n", "I was working on a project which used the GPU instead of CPU. I did some Googling and found out that the best way to do this is by using the Anaconda. As you can see I ended up here due to errors I faced. All the answers in this tread is insightful but the recent working answer that has worked for me is a combination previous answers. I'll try to sum up one simple answer and step that can be followed for developers like me to solve this issue.\n\nChange the path in system settings> advanced system settings > Environment Variables> System variables > path > edit > add \"C:ProgramData\\Anaconda3\\Scripts\"\n\nMake sure the python is 3.4+\n\nOpen CMD in admin mode, change directory to the anaconda then type\nconda update conda\nif there is SSL error type\nconda config --set ssl_verify no or\nconda config --set ssl_verify false\n\nThen type conda update anaconda-navigator\n\nThen to avoid HTTP and SSL error. Copy the following files:\n\n\nlibcrypto-1_1-x64.* (.dll and .pdb files) libssl-1_1-x64.* (.dll and .pdb files)\nFrom C:ProgramData\\Anaconda3\\Library\\bin to C:ProgramData\\Anaconda3\\DLLs.\n\nNow open Anaconda prompt(Anaconda3) type(one by one)\npip install PySide2\npip uninstall pyqt\npip uninstall PyQt5\n\n\n6.Then while in Anaconda3 type pip install pyqt test if you can open anaconda-navigator in anaconda prompt. (if it works you're almost there)\n\nClose everything, Fire up the CMD in Admin mode and type pip install PyQt5.\n\nOpen anaconda-navigator in CMD. if It is not working properly, repeat the process after step 4.\n\n\nI'm not a very pro coder. but this is the process that worked for me!\nGood luck!!\n", "Tried all of the other answers, but didn't work for me.\nWhat worked for me was this sequence:\npip uninstall pyside2\nconda uninstall qt\nconda update conda\nconda install anaconda-navigator\n\n" ]

[ 40, 5, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]

[]

[]

[ "anaconda", "python", "spyder" ]

[ "You know how to add one filter. There is no such thing as \"additional\" filters. Just add your conditions to the current condition. Since you want all of the conditions to be True to select a record, you'd use the boolean and logic. For example:\nfiltered = (\n col[\"Title\"] \n for col in collections \n if col[\"Awards\"][0] == \"Y\"\n and col[\"Actor\"].split(\", \")[-1] == \"Richard\"\n and int(col[\"Year\"]) < 1985\n )\n\nNotice I added an int() around the col[\"Year\"] to convert it to an integer.\n\nYou've actually gone and reinvented csv.DictReader in the setup to this problem! Instead of\nfile_name = \"film.csv\"\nlines = (line for line in open(file_name,encoding='cp1252')) #generator to capture lines\nlists = (s.rstrip().split(\";\") for s in lines) #generators to capture lists containing values from lines\n\n#browse lists and index them per header values, then filter all movies that have been awarded\n#using a new generator object\n\ncols=next(lists) #obtains only the header\nprint(cols)\ncollections = (dict(zip(cols,data)) for data in lists)\nfiltered = ...\n\nYou could have just done:\nimport csv\n\nfile_name = \"film.csv\"\nwith open(file_name) as f:\n collections = csv.DictReader(delimiter=\";\")\n filtered = ...\n\n" ]

[ 0 ]

[]

[]

[ "csv", "python" ]

[ "x = [121,13,155551]\n\ndef palindrome_check(x):\n res = []\n for num_from__list in x:\n res.append(str(num_from__list) == str(num_from__list)[::-1])\n return res\n\nprint(palindrome_check(x))\n\nor even better:\nx = [121,13,155551]\n\ndef palindrome_check(x):\n return [str(num_from__list) == str(num_from__list[::-1]) for num_from__list in x]\n\nprint(palindrome_check(x))\n\n", "You can change your palindrome_check function to take in a single number instead of an array of numbers and then use a list comprehension to get the results into a new list:\ndef palindrome_check(num):\n return str(num) == str(num)[::-1]\n\nnumbers = [121, 13, 155551]\n\nresults = [palindrome_check(num) for num in numbers]\nprint(results)\n\nRight now your function returns True or False based on only the first number in the list it received, which is probably not what you want.\n", "You can replace return by yield hence transforming your function into a generator.\ndef palindrome_check(x):\n for num_from__list in x:\n if str(num_from__list) == str(num_from__list[::-1]):\n yield True\n else:\n yield False\n\nprint(list(palindrome_check(x)))\n\n", "I would go for the following:\ndef palindrome_check(x):\n return \" \".join([str(str(n) == str(n)[::-1]) for n in x])\n\nExplanation:\n\nFor loop\nmyList = [function(n) for n in x]\n# same as\nmyList = list()\nfor n in x:\n myList.append(function(n))\n# eg. [n for n in x] === [121, 13, 155551]\n\n\nConvert number to string and its mirror\n# number\nn # eg. 123\n# string\nstr(n) # eg. '123'\n# mirrored string\nstr(n)[::-1] # eg. '321'\n# eg. [str(n) for n in x] === ['121', '13', '155551']\n# [str(n)[::-1] for n in x] === ['121', '31', '155551']\n\n\nCompare string value with its mirror\nstr(n) == str(n)[::-1] # True/False\n# eg. [str(n) == str(n)[::-1] for n in x] === [True, False, True]\n\n\nStringify boolean for concatenation (join)\nstr(str(n) == str(n)[::-1]) # 'True'/'False'\n# eg. [str(str(n) == str(n)[::-1]) for n in x] === ['True', 'False', 'True']\n\n\nConcatenate string elements of a list\n# eg. \"xx\".join([\"A\", \"B\", \"C\"]) returns 'AxxBxxC'\n\" \".join([str(str(n) == str(n)[::-1]) for n in x]) # 'True False True'\n\n\n\n" ]

[ 1, 1, 1, 0 ]

[]

[]

[ "python" ]

[ "Every time you run in your for loop:\noutlines = fileReader.getOutlines()\nyou are overwriting the contents of outlines not appending to it. So it is not surprising you end up with only the last.\nWhat is the type of outlines? Is it a list or similar? Consult the PyPDF2 documentation to find out, and work out how to build the appended outlines. Then adding the top level entries is your second task.\n", "for anyone wondering this is the solution however all bookmarks from the merged pdfs start on page 1 (or 0 in code speak) anyone with a solution to this im all ears :)\nrecord = []\nfor file in onlyfiles:\n fileReader = PdfFileReader(open(file,'rb'))\n outlines = fileReader.getOutlines()\n record.append(outlines)\n\n\nmerger = PdfFileMerger(strict=False) \n\nfor item in os.listdir(source_dir):\n if item.endswith('pdf'): \n merger.bookmarks=record\n merger.append(item)\n \nmerger.write('merged.pdf')\nmerger.close() \n\n" ]

[ 0, 0 ]

[]

[]

[ "bookmarks", "pypdf2", "python" ]