path
stringlengths 13
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sequencelengths 1
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stringlengths 0
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stringclasses 1
value |
|---|---|---|---|
16144426/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier,AdaBoostClassifier
from sklearn.metrics import accuracy_score
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.metrics import accuracy_score
model = RandomForestClassifier(n_estimators=800)
model.fit(xtrain, ytrain)
test_pred = model.predict(xtest)
accuracy_score(ytest, test_pred) | code |
16144426/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import gensim
import nltk
import os
print(os.listdir('../input/embeddings/GoogleNews-vectors-negative300/')) | code |
16144426/cell_7 | [
"text_plain_output_1.png"
] | import gensim
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/embeddings/GoogleNews-vectors-negative300/GoogleNews-vectors-negative300.bin'
embeddings = gensim.models.KeyedVectors.load_word2vec_format(path, binary=True)
pd.Series(embeddings['modi'][:5])
url = 'https://bit.ly/2S2yXEd'
data = pd.read_csv(url)
doc1 = data.iloc[0, 0]
print(doc1)
print(nltk.word_tokenize(doc1.lower())) | code |
16144426/cell_28 | [
"text_plain_output_1.png"
] | from nltk.stem import PorterStemmer
from nltk.stem import PorterStemmer
import gensim
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/embeddings/GoogleNews-vectors-negative300/GoogleNews-vectors-negative300.bin'
embeddings = gensim.models.KeyedVectors.load_word2vec_format(path, binary=True)
pd.Series(embeddings['modi'][:5])
embeddings.most_similar('modi', topn=10)
url = 'https://bit.ly/2S2yXEd'
data = pd.read_csv(url)
doc1 = data.iloc[0, 0]
docs = data['review']
words = nltk.word_tokenize(doc1.lower())
temp = pd.DataFrame()
for word in words:
try:
temp = temp.append(pd.Series(embeddings[word][:5]), ignore_index=True)
except:
docs = docs.str.lower().str.replace('[^a-z ]', '')
from nltk.stem import PorterStemmer
stemmer = PorterStemmer()
stopwords = nltk.corpus.stopwords.words('english')
def clean_doc(doc):
words = doc.split(' ')
words_clean = [word for word in words if word not in stopwords]
doc_clean = ' '.join(words_clean)
return doc_clean
docs_clean = docs.apply(clean_doc)
docs_clean.shape
docs_vectors = pd.DataFrame()
for doc in docs_clean:
words = nltk.word_tokenize(doc)
temp = pd.DataFrame()
for word in words:
try:
word_vec = embeddings[word]
temp = temp.append(pd.Series(word_vec), ignore_index=True)
except:
pass
docs_vectors = docs_vectors.append(temp.mean(), ignore_index=True)
docs_vectors.shape
pd.isnull(docs_vectors).sum(axis=1).sort_values(ascending=False).head()
url = 'https://bit.ly/2W21FY7'
data = pd.read_csv(url)
data.shape
docs = data.loc[:, 'Lower_Case_Reviews']
docs = docs.str.lower().str.replace('[^a-z ]', '')
from nltk.stem import PorterStemmer
stemmer = PorterStemmer()
stopwords = nltk.corpus.stopwords.words('english')
def clean_doc(doc):
words = doc.split(' ')
words_clean = [stemmer.stem(word) for word in words if word not in stopwords]
doc_clean = ' '.join(words_clean)
return doc_clean
docs_clean = docs.apply(clean_doc)
docs_clean.head() | code |
16144426/cell_8 | [
"text_plain_output_1.png"
] | import gensim
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/embeddings/GoogleNews-vectors-negative300/GoogleNews-vectors-negative300.bin'
embeddings = gensim.models.KeyedVectors.load_word2vec_format(path, binary=True)
pd.Series(embeddings['modi'][:5])
url = 'https://bit.ly/2S2yXEd'
data = pd.read_csv(url)
doc1 = data.iloc[0, 0]
docs = data['review']
docs.head() | code |
16144426/cell_15 | [
"text_plain_output_1.png"
] | from nltk.stem import PorterStemmer
import gensim
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/embeddings/GoogleNews-vectors-negative300/GoogleNews-vectors-negative300.bin'
embeddings = gensim.models.KeyedVectors.load_word2vec_format(path, binary=True)
pd.Series(embeddings['modi'][:5])
embeddings.most_similar('modi', topn=10)
url = 'https://bit.ly/2S2yXEd'
data = pd.read_csv(url)
doc1 = data.iloc[0, 0]
docs = data['review']
words = nltk.word_tokenize(doc1.lower())
temp = pd.DataFrame()
for word in words:
try:
temp = temp.append(pd.Series(embeddings[word][:5]), ignore_index=True)
except:
docs = docs.str.lower().str.replace('[^a-z ]', '')
from nltk.stem import PorterStemmer
stemmer = PorterStemmer()
stopwords = nltk.corpus.stopwords.words('english')
def clean_doc(doc):
words = doc.split(' ')
words_clean = [word for word in words if word not in stopwords]
doc_clean = ' '.join(words_clean)
return doc_clean
docs_clean = docs.apply(clean_doc)
docs_clean.shape
docs_vectors = pd.DataFrame()
for doc in docs_clean:
words = nltk.word_tokenize(doc)
temp = pd.DataFrame()
for word in words:
try:
word_vec = embeddings[word]
temp = temp.append(pd.Series(word_vec), ignore_index=True)
except:
pass
docs_vectors = docs_vectors.append(temp.mean(), ignore_index=True)
docs_vectors.shape
docs_vectors.head() | code |
16144426/cell_16 | [
"text_plain_output_1.png"
] | from nltk.stem import PorterStemmer
import gensim
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/embeddings/GoogleNews-vectors-negative300/GoogleNews-vectors-negative300.bin'
embeddings = gensim.models.KeyedVectors.load_word2vec_format(path, binary=True)
pd.Series(embeddings['modi'][:5])
embeddings.most_similar('modi', topn=10)
url = 'https://bit.ly/2S2yXEd'
data = pd.read_csv(url)
doc1 = data.iloc[0, 0]
docs = data['review']
words = nltk.word_tokenize(doc1.lower())
temp = pd.DataFrame()
for word in words:
try:
temp = temp.append(pd.Series(embeddings[word][:5]), ignore_index=True)
except:
docs = docs.str.lower().str.replace('[^a-z ]', '')
from nltk.stem import PorterStemmer
stemmer = PorterStemmer()
stopwords = nltk.corpus.stopwords.words('english')
def clean_doc(doc):
words = doc.split(' ')
words_clean = [word for word in words if word not in stopwords]
doc_clean = ' '.join(words_clean)
return doc_clean
docs_clean = docs.apply(clean_doc)
docs_clean.shape
docs_vectors = pd.DataFrame()
for doc in docs_clean:
words = nltk.word_tokenize(doc)
temp = pd.DataFrame()
for word in words:
try:
word_vec = embeddings[word]
temp = temp.append(pd.Series(word_vec), ignore_index=True)
except:
pass
docs_vectors = docs_vectors.append(temp.mean(), ignore_index=True)
docs_vectors.shape
pd.isnull(docs_vectors).sum(axis=1).sort_values(ascending=False).head() | code |
16144426/cell_3 | [
"text_html_output_1.png"
] | import gensim
path = '../input/embeddings/GoogleNews-vectors-negative300/GoogleNews-vectors-negative300.bin'
embeddings = gensim.models.KeyedVectors.load_word2vec_format(path, binary=True)
list(embeddings['modi'][:5]) | code |
16144426/cell_31 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from nltk.stem import PorterStemmer
from nltk.stem import PorterStemmer
from sklearn.feature_extraction.text import CountVectorizer
import gensim
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/embeddings/GoogleNews-vectors-negative300/GoogleNews-vectors-negative300.bin'
embeddings = gensim.models.KeyedVectors.load_word2vec_format(path, binary=True)
pd.Series(embeddings['modi'][:5])
embeddings.most_similar('modi', topn=10)
url = 'https://bit.ly/2S2yXEd'
data = pd.read_csv(url)
doc1 = data.iloc[0, 0]
docs = data['review']
words = nltk.word_tokenize(doc1.lower())
temp = pd.DataFrame()
for word in words:
try:
temp = temp.append(pd.Series(embeddings[word][:5]), ignore_index=True)
except:
docs = docs.str.lower().str.replace('[^a-z ]', '')
from nltk.stem import PorterStemmer
stemmer = PorterStemmer()
stopwords = nltk.corpus.stopwords.words('english')
def clean_doc(doc):
words = doc.split(' ')
words_clean = [word for word in words if word not in stopwords]
doc_clean = ' '.join(words_clean)
return doc_clean
docs_clean = docs.apply(clean_doc)
docs_clean.shape
docs_vectors = pd.DataFrame()
for doc in docs_clean:
words = nltk.word_tokenize(doc)
temp = pd.DataFrame()
for word in words:
try:
word_vec = embeddings[word]
temp = temp.append(pd.Series(word_vec), ignore_index=True)
except:
pass
docs_vectors = docs_vectors.append(temp.mean(), ignore_index=True)
docs_vectors.shape
pd.isnull(docs_vectors).sum(axis=1).sort_values(ascending=False).head()
X = docs_vectors.drop([64, 590])
Y = data['sentiment'].drop([64, 590])
url = 'https://bit.ly/2W21FY7'
data = pd.read_csv(url)
data.shape
docs = data.loc[:, 'Lower_Case_Reviews']
Y = data['Sentiment_Manual']
Y.value_counts()
docs = docs.str.lower().str.replace('[^a-z ]', '')
from nltk.stem import PorterStemmer
stemmer = PorterStemmer()
stopwords = nltk.corpus.stopwords.words('english')
def clean_doc(doc):
words = doc.split(' ')
words_clean = [stemmer.stem(word) for word in words if word not in stopwords]
doc_clean = ' '.join(words_clean)
return doc_clean
docs_clean = docs.apply(clean_doc)
X = docs_clean
(X.shape, Y.shape)
from sklearn.feature_extraction.text import CountVectorizer
cv = CountVectorizer(min_df=5)
cv.fit(X) | code |
16144426/cell_24 | [
"text_plain_output_1.png"
] | from nltk.stem import PorterStemmer
import gensim
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/embeddings/GoogleNews-vectors-negative300/GoogleNews-vectors-negative300.bin'
embeddings = gensim.models.KeyedVectors.load_word2vec_format(path, binary=True)
pd.Series(embeddings['modi'][:5])
embeddings.most_similar('modi', topn=10)
url = 'https://bit.ly/2S2yXEd'
data = pd.read_csv(url)
doc1 = data.iloc[0, 0]
docs = data['review']
words = nltk.word_tokenize(doc1.lower())
temp = pd.DataFrame()
for word in words:
try:
temp = temp.append(pd.Series(embeddings[word][:5]), ignore_index=True)
except:
docs = docs.str.lower().str.replace('[^a-z ]', '')
from nltk.stem import PorterStemmer
stemmer = PorterStemmer()
stopwords = nltk.corpus.stopwords.words('english')
def clean_doc(doc):
words = doc.split(' ')
words_clean = [word for word in words if word not in stopwords]
doc_clean = ' '.join(words_clean)
return doc_clean
docs_clean = docs.apply(clean_doc)
docs_clean.shape
docs_vectors = pd.DataFrame()
for doc in docs_clean:
words = nltk.word_tokenize(doc)
temp = pd.DataFrame()
for word in words:
try:
word_vec = embeddings[word]
temp = temp.append(pd.Series(word_vec), ignore_index=True)
except:
pass
docs_vectors = docs_vectors.append(temp.mean(), ignore_index=True)
docs_vectors.shape
pd.isnull(docs_vectors).sum(axis=1).sort_values(ascending=False).head()
url = 'https://bit.ly/2W21FY7'
data = pd.read_csv(url)
data.shape
docs = data.loc[:, 'Lower_Case_Reviews']
print(docs.shape)
docs.head() | code |
16144426/cell_14 | [
"text_html_output_1.png"
] | from nltk.stem import PorterStemmer
import gensim
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/embeddings/GoogleNews-vectors-negative300/GoogleNews-vectors-negative300.bin'
embeddings = gensim.models.KeyedVectors.load_word2vec_format(path, binary=True)
pd.Series(embeddings['modi'][:5])
embeddings.most_similar('modi', topn=10)
url = 'https://bit.ly/2S2yXEd'
data = pd.read_csv(url)
doc1 = data.iloc[0, 0]
docs = data['review']
words = nltk.word_tokenize(doc1.lower())
temp = pd.DataFrame()
for word in words:
try:
temp = temp.append(pd.Series(embeddings[word][:5]), ignore_index=True)
except:
docs = docs.str.lower().str.replace('[^a-z ]', '')
from nltk.stem import PorterStemmer
stemmer = PorterStemmer()
stopwords = nltk.corpus.stopwords.words('english')
def clean_doc(doc):
words = doc.split(' ')
words_clean = [word for word in words if word not in stopwords]
doc_clean = ' '.join(words_clean)
return doc_clean
docs_clean = docs.apply(clean_doc)
docs_clean.shape
docs_vectors = pd.DataFrame()
for doc in docs_clean:
words = nltk.word_tokenize(doc)
temp = pd.DataFrame()
for word in words:
try:
word_vec = embeddings[word]
temp = temp.append(pd.Series(word_vec), ignore_index=True)
except:
pass
docs_vectors = docs_vectors.append(temp.mean(), ignore_index=True)
docs_vectors.shape | code |
16144426/cell_22 | [
"text_plain_output_1.png"
] | from nltk.stem import PorterStemmer
import gensim
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/embeddings/GoogleNews-vectors-negative300/GoogleNews-vectors-negative300.bin'
embeddings = gensim.models.KeyedVectors.load_word2vec_format(path, binary=True)
pd.Series(embeddings['modi'][:5])
embeddings.most_similar('modi', topn=10)
url = 'https://bit.ly/2S2yXEd'
data = pd.read_csv(url)
doc1 = data.iloc[0, 0]
docs = data['review']
words = nltk.word_tokenize(doc1.lower())
temp = pd.DataFrame()
for word in words:
try:
temp = temp.append(pd.Series(embeddings[word][:5]), ignore_index=True)
except:
docs = docs.str.lower().str.replace('[^a-z ]', '')
from nltk.stem import PorterStemmer
stemmer = PorterStemmer()
stopwords = nltk.corpus.stopwords.words('english')
def clean_doc(doc):
words = doc.split(' ')
words_clean = [word for word in words if word not in stopwords]
doc_clean = ' '.join(words_clean)
return doc_clean
docs_clean = docs.apply(clean_doc)
docs_clean.shape
docs_vectors = pd.DataFrame()
for doc in docs_clean:
words = nltk.word_tokenize(doc)
temp = pd.DataFrame()
for word in words:
try:
word_vec = embeddings[word]
temp = temp.append(pd.Series(word_vec), ignore_index=True)
except:
pass
docs_vectors = docs_vectors.append(temp.mean(), ignore_index=True)
docs_vectors.shape
pd.isnull(docs_vectors).sum(axis=1).sort_values(ascending=False).head()
url = 'https://bit.ly/2W21FY7'
data = pd.read_csv(url)
data.shape | code |
16144426/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import gensim
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/embeddings/GoogleNews-vectors-negative300/GoogleNews-vectors-negative300.bin'
embeddings = gensim.models.KeyedVectors.load_word2vec_format(path, binary=True)
pd.Series(embeddings['modi'][:5])
embeddings.most_similar('modi', topn=10)
url = 'https://bit.ly/2S2yXEd'
data = pd.read_csv(url)
doc1 = data.iloc[0, 0]
words = nltk.word_tokenize(doc1.lower())
temp = pd.DataFrame()
for word in words:
try:
temp = temp.append(pd.Series(embeddings[word][:5]), ignore_index=True)
except:
temp | code |
16144426/cell_27 | [
"text_plain_output_1.png"
] | from nltk.stem import PorterStemmer
import gensim
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/embeddings/GoogleNews-vectors-negative300/GoogleNews-vectors-negative300.bin'
embeddings = gensim.models.KeyedVectors.load_word2vec_format(path, binary=True)
pd.Series(embeddings['modi'][:5])
embeddings.most_similar('modi', topn=10)
url = 'https://bit.ly/2S2yXEd'
data = pd.read_csv(url)
doc1 = data.iloc[0, 0]
docs = data['review']
words = nltk.word_tokenize(doc1.lower())
temp = pd.DataFrame()
for word in words:
try:
temp = temp.append(pd.Series(embeddings[word][:5]), ignore_index=True)
except:
docs = docs.str.lower().str.replace('[^a-z ]', '')
from nltk.stem import PorterStemmer
stemmer = PorterStemmer()
stopwords = nltk.corpus.stopwords.words('english')
def clean_doc(doc):
words = doc.split(' ')
words_clean = [word for word in words if word not in stopwords]
doc_clean = ' '.join(words_clean)
return doc_clean
docs_clean = docs.apply(clean_doc)
docs_clean.shape
docs_vectors = pd.DataFrame()
for doc in docs_clean:
words = nltk.word_tokenize(doc)
temp = pd.DataFrame()
for word in words:
try:
word_vec = embeddings[word]
temp = temp.append(pd.Series(word_vec), ignore_index=True)
except:
pass
docs_vectors = docs_vectors.append(temp.mean(), ignore_index=True)
docs_vectors.shape
pd.isnull(docs_vectors).sum(axis=1).sort_values(ascending=False).head()
url = 'https://bit.ly/2W21FY7'
data = pd.read_csv(url)
data.shape
docs = data.loc[:, 'Lower_Case_Reviews']
docs = docs.str.lower().str.replace('[^a-z ]', '')
docs.head() | code |
16144426/cell_12 | [
"text_plain_output_1.png"
] | from nltk.stem import PorterStemmer
import gensim
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/embeddings/GoogleNews-vectors-negative300/GoogleNews-vectors-negative300.bin'
embeddings = gensim.models.KeyedVectors.load_word2vec_format(path, binary=True)
pd.Series(embeddings['modi'][:5])
embeddings.most_similar('modi', topn=10)
url = 'https://bit.ly/2S2yXEd'
data = pd.read_csv(url)
doc1 = data.iloc[0, 0]
docs = data['review']
words = nltk.word_tokenize(doc1.lower())
temp = pd.DataFrame()
for word in words:
try:
temp = temp.append(pd.Series(embeddings[word][:5]), ignore_index=True)
except:
docs = docs.str.lower().str.replace('[^a-z ]', '')
from nltk.stem import PorterStemmer
stemmer = PorterStemmer()
stopwords = nltk.corpus.stopwords.words('english')
def clean_doc(doc):
words = doc.split(' ')
words_clean = [word for word in words if word not in stopwords]
doc_clean = ' '.join(words_clean)
return doc_clean
docs_clean = docs.apply(clean_doc)
docs_clean.head() | code |
16144426/cell_5 | [
"text_plain_output_1.png"
] | import gensim
path = '../input/embeddings/GoogleNews-vectors-negative300/GoogleNews-vectors-negative300.bin'
embeddings = gensim.models.KeyedVectors.load_word2vec_format(path, binary=True)
embeddings.most_similar('modi', topn=10) | code |
16123290/cell_21 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import plotly.graph_objs as go
import plotly.graph_objs as go
df = pd.read_csv('../input/PJME_hourly.csv', index_col=[0], parse_dates=[0])
import plotly.graph_objs as go
trace1 = go.Scatter(x=df.index, y=df.PJME_MW)
data = [trace1]
fig = dict(data=data)
splitdate = '2014-01-01'
df_train_set = df[df.index < splitdate]
df_test_set = df[df.index > splitdate]
trace1 = go.Scatter(x=df_train_set.index, y=df_train_set.PJME_MW)
data = [trace1]
fig = dict(data=data)
trace1 = go.Scatter(x=df_test_set.index, y=df_test_set.PJME_MW)
data = [trace1]
fig = dict(data=data)
def AddDateProperties(df):
df['date'] = df.index
df['hour'] = df['date'].dt.hour
df['dayofweek'] = df['date'].dt.dayofweek
df['quarter'] = df['date'].dt.quarter
df['month'] = df['date'].dt.month
df['year'] = df['date'].dt.year
df['dayofyear'] = df['date'].dt.dayofyear
df['dayofmonth'] = df['date'].dt.day
df['weekofyear'] = df['date'].dt.weekofyear
return df
df_test_set = AddDateProperties(df_test_set)
df_train_set = AddDateProperties(df_train_set)
df_test_set = df_test_set.drop(['date'], axis=1)
df_train_set = df_train_set.drop(['date'], axis=1)
y_test = df_test_set['PJME_MW']
y_train = df_train_set['PJME_MW']
X_test = df_test_set.loc[:, df_test_set.columns != 'PJME_MW']
X_train = df_train_set.loc[:, df_train_set.columns != 'PJME_MW']
df_test_set.head() | code |
16123290/cell_9 | [
"image_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import plotly.graph_objs as go
import plotly.graph_objs as go
df = pd.read_csv('../input/PJME_hourly.csv', index_col=[0], parse_dates=[0])
import plotly.graph_objs as go
trace1 = go.Scatter(x=df.index, y=df.PJME_MW)
data = [trace1]
fig = dict(data=data)
splitdate = '2014-01-01'
df_train_set = df[df.index < splitdate]
df_test_set = df[df.index > splitdate]
trace1 = go.Scatter(x=df_train_set.index, y=df_train_set.PJME_MW)
data = [trace1]
fig = dict(data=data)
trace1 = go.Scatter(x=df_test_set.index, y=df_test_set.PJME_MW)
data = [trace1]
fig = dict(data=data)
iplot(fig) | code |
16123290/cell_4 | [
"text_html_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import plotly.graph_objs as go
import plotly.graph_objs as go
df = pd.read_csv('../input/PJME_hourly.csv', index_col=[0], parse_dates=[0])
import plotly.graph_objs as go
trace1 = go.Scatter(x=df.index, y=df.PJME_MW)
data = [trace1]
fig = dict(data=data)
iplot(fig) | code |
16123290/cell_20 | [
"text_html_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import matplotlib.pyplot as plt
import pandas as pd
import plotly.graph_objs as go
import plotly.graph_objs as go
df = pd.read_csv('../input/PJME_hourly.csv', index_col=[0], parse_dates=[0])
import plotly.graph_objs as go
trace1 = go.Scatter(x=df.index, y=df.PJME_MW)
data = [trace1]
fig = dict(data=data)
splitdate = '2014-01-01'
df_train_set = df[df.index < splitdate]
df_test_set = df[df.index > splitdate]
trace1 = go.Scatter(x=df_train_set.index, y=df_train_set.PJME_MW)
data = [trace1]
fig = dict(data=data)
trace1 = go.Scatter(x=df_test_set.index, y=df_test_set.PJME_MW)
data = [trace1]
fig = dict(data=data)
def AddDateProperties(df):
df['date'] = df.index
df['hour'] = df['date'].dt.hour
df['dayofweek'] = df['date'].dt.dayofweek
df['quarter'] = df['date'].dt.quarter
df['month'] = df['date'].dt.month
df['year'] = df['date'].dt.year
df['dayofyear'] = df['date'].dt.dayofyear
df['dayofmonth'] = df['date'].dt.day
df['weekofyear'] = df['date'].dt.weekofyear
return df
df_test_set = AddDateProperties(df_test_set)
df_train_set = AddDateProperties(df_train_set)
df_test_set = df_test_set.drop(['date'], axis=1)
df_train_set = df_train_set.drop(['date'], axis=1)
y_test = df_test_set['PJME_MW']
y_train = df_train_set['PJME_MW']
X_test = df_test_set.loc[:, df_test_set.columns != 'PJME_MW']
X_train = df_train_set.loc[:, df_train_set.columns != 'PJME_MW']
df_all = pd.concat([df_test_set, df_train_set], sort=False)
start_date = '2014-01-01'
end_date = '2014-01-31'
fig, ax = plt.subplots(1)
df_all[['PJME_MW', 'MW_Prediction']].plot(ax=ax, figsize=(15, 5), style=['.'])
ax.set_xbound(lower=start_date, upper=end_date) | code |
16123290/cell_29 | [
"image_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import matplotlib.pyplot as plt
import pandas as pd
import plotly.graph_objs as go
import plotly.graph_objs as go
df = pd.read_csv('../input/PJME_hourly.csv', index_col=[0], parse_dates=[0])
import plotly.graph_objs as go
trace1 = go.Scatter(x=df.index, y=df.PJME_MW)
data = [trace1]
fig = dict(data=data)
splitdate = '2014-01-01'
df_train_set = df[df.index < splitdate]
df_test_set = df[df.index > splitdate]
trace1 = go.Scatter(x=df_train_set.index, y=df_train_set.PJME_MW)
data = [trace1]
fig = dict(data=data)
trace1 = go.Scatter(x=df_test_set.index, y=df_test_set.PJME_MW)
data = [trace1]
fig = dict(data=data)
def AddDateProperties(df):
df['date'] = df.index
df['hour'] = df['date'].dt.hour
df['dayofweek'] = df['date'].dt.dayofweek
df['quarter'] = df['date'].dt.quarter
df['month'] = df['date'].dt.month
df['year'] = df['date'].dt.year
df['dayofyear'] = df['date'].dt.dayofyear
df['dayofmonth'] = df['date'].dt.day
df['weekofyear'] = df['date'].dt.weekofyear
return df
df_test_set = AddDateProperties(df_test_set)
df_train_set = AddDateProperties(df_train_set)
df_test_set = df_test_set.drop(['date'], axis=1)
df_train_set = df_train_set.drop(['date'], axis=1)
y_test = df_test_set['PJME_MW']
y_train = df_train_set['PJME_MW']
X_test = df_test_set.loc[:, df_test_set.columns != 'PJME_MW']
X_train = df_train_set.loc[:, df_train_set.columns != 'PJME_MW']
df_all = pd.concat([df_test_set, df_train_set], sort=False)
start_date ='2014-01-01'
end_date = '2014-01-31'
fig,ax = plt.subplots(1)
df_all[['PJME_MW' , 'MW_Prediction']].plot(ax=ax , figsize = (15,5 ) , style = ['.']);
ax.set_xbound(lower=start_date , upper= end_date)
start_date = '2015-02-20 00:00:00'
end_date = '2015-02-20 23:00:00'
fig, ax = plt.subplots(1)
df_all[['PJME_MW', 'MW_Prediction']].plot(ax=ax, figsize=(15, 5), style=['.'])
ax.set_xbound(lower=start_date, upper=end_date) | code |
16123290/cell_26 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import numpy as np
import pandas as pd
import plotly.graph_objs as go
import plotly.graph_objs as go
df = pd.read_csv('../input/PJME_hourly.csv', index_col=[0], parse_dates=[0])
import plotly.graph_objs as go
trace1 = go.Scatter(x=df.index, y=df.PJME_MW)
data = [trace1]
fig = dict(data=data)
splitdate = '2014-01-01'
df_train_set = df[df.index < splitdate]
df_test_set = df[df.index > splitdate]
trace1 = go.Scatter(x=df_train_set.index, y=df_train_set.PJME_MW)
data = [trace1]
fig = dict(data=data)
trace1 = go.Scatter(x=df_test_set.index, y=df_test_set.PJME_MW)
data = [trace1]
fig = dict(data=data)
def AddDateProperties(df):
df['date'] = df.index
df['hour'] = df['date'].dt.hour
df['dayofweek'] = df['date'].dt.dayofweek
df['quarter'] = df['date'].dt.quarter
df['month'] = df['date'].dt.month
df['year'] = df['date'].dt.year
df['dayofyear'] = df['date'].dt.dayofyear
df['dayofmonth'] = df['date'].dt.day
df['weekofyear'] = df['date'].dt.weekofyear
return df
df_test_set = AddDateProperties(df_test_set)
df_train_set = AddDateProperties(df_train_set)
df_test_set = df_test_set.drop(['date'], axis=1)
df_train_set = df_train_set.drop(['date'], axis=1)
y_test = df_test_set['PJME_MW']
y_train = df_train_set['PJME_MW']
X_test = df_test_set.loc[:, df_test_set.columns != 'PJME_MW']
X_train = df_train_set.loc[:, df_train_set.columns != 'PJME_MW']
df_test_set['AbsError'] = df_test_set.Error.apply(np.abs)
day_groupby = df_test_set.groupby(['year', 'month', 'dayofmonth'])
error_by_day = day_groupby['PJME_MW', 'MW_Prediction', 'Error', 'AbsError'].mean()
error_by_day.sort_values(ascending=True, by='AbsError').head(15) | code |
16123290/cell_11 | [
"image_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import plotly.graph_objs as go
import plotly.graph_objs as go
df = pd.read_csv('../input/PJME_hourly.csv', index_col=[0], parse_dates=[0])
import plotly.graph_objs as go
trace1 = go.Scatter(x=df.index, y=df.PJME_MW)
data = [trace1]
fig = dict(data=data)
splitdate = '2014-01-01'
df_train_set = df[df.index < splitdate]
df_test_set = df[df.index > splitdate]
trace1 = go.Scatter(x=df_train_set.index, y=df_train_set.PJME_MW)
data = [trace1]
fig = dict(data=data)
trace1 = go.Scatter(x=df_test_set.index, y=df_test_set.PJME_MW)
data = [trace1]
fig = dict(data=data)
def AddDateProperties(df):
df['date'] = df.index
df['hour'] = df['date'].dt.hour
df['dayofweek'] = df['date'].dt.dayofweek
df['quarter'] = df['date'].dt.quarter
df['month'] = df['date'].dt.month
df['year'] = df['date'].dt.year
df['dayofyear'] = df['date'].dt.dayofyear
df['dayofmonth'] = df['date'].dt.day
df['weekofyear'] = df['date'].dt.weekofyear
return df
df_test_set = AddDateProperties(df_test_set)
df_train_set = AddDateProperties(df_train_set)
df_test_set = df_test_set.drop(['date'], axis=1)
df_train_set = df_train_set.drop(['date'], axis=1)
y_test = df_test_set['PJME_MW']
y_train = df_train_set['PJME_MW']
X_test = df_test_set.loc[:, df_test_set.columns != 'PJME_MW']
X_train = df_train_set.loc[:, df_train_set.columns != 'PJME_MW'] | code |
16123290/cell_1 | [
"text_plain_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import xgboost as xgb
from xgboost import plot_importance, plot_tree
from sklearn.metrics import mean_squared_error, mean_absolute_error
import plotly.plotly as py
from plotly.offline import init_notebook_mode, iplot
init_notebook_mode(connected=True)
import plotly.graph_objs as go
import os
print(os.listdir('../input')) | code |
16123290/cell_7 | [
"image_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import plotly.graph_objs as go
import plotly.graph_objs as go
df = pd.read_csv('../input/PJME_hourly.csv', index_col=[0], parse_dates=[0])
import plotly.graph_objs as go
trace1 = go.Scatter(x=df.index, y=df.PJME_MW)
data = [trace1]
fig = dict(data=data)
splitdate = '2014-01-01'
df_train_set = df[df.index < splitdate]
df_test_set = df[df.index > splitdate]
df_test_set.head(2) | code |
16123290/cell_18 | [
"text_html_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import plotly.graph_objs as go
import plotly.graph_objs as go
df = pd.read_csv('../input/PJME_hourly.csv', index_col=[0], parse_dates=[0])
import plotly.graph_objs as go
trace1 = go.Scatter(x=df.index, y=df.PJME_MW)
data = [trace1]
fig = dict(data=data)
splitdate = '2014-01-01'
df_train_set = df[df.index < splitdate]
df_test_set = df[df.index > splitdate]
trace1 = go.Scatter(x=df_train_set.index, y=df_train_set.PJME_MW)
data = [trace1]
fig = dict(data=data)
trace1 = go.Scatter(x=df_test_set.index, y=df_test_set.PJME_MW)
data = [trace1]
fig = dict(data=data)
def AddDateProperties(df):
df['date'] = df.index
df['hour'] = df['date'].dt.hour
df['dayofweek'] = df['date'].dt.dayofweek
df['quarter'] = df['date'].dt.quarter
df['month'] = df['date'].dt.month
df['year'] = df['date'].dt.year
df['dayofyear'] = df['date'].dt.dayofyear
df['dayofmonth'] = df['date'].dt.day
df['weekofyear'] = df['date'].dt.weekofyear
return df
df_test_set = AddDateProperties(df_test_set)
df_train_set = AddDateProperties(df_train_set)
df_test_set = df_test_set.drop(['date'], axis=1)
df_train_set = df_train_set.drop(['date'], axis=1)
y_test = df_test_set['PJME_MW']
y_train = df_train_set['PJME_MW']
X_test = df_test_set.loc[:, df_test_set.columns != 'PJME_MW']
X_train = df_train_set.loc[:, df_train_set.columns != 'PJME_MW']
df_all = pd.concat([df_test_set, df_train_set], sort=False)
df_all[['PJME_MW', 'MW_Prediction']].plot(figsize=(15, 5)) | code |
16123290/cell_32 | [
"text_html_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
from xgboost import plot_importance, plot_tree
import matplotlib.pyplot as plt
import pandas as pd
import plotly.graph_objs as go
import plotly.graph_objs as go
import xgboost as xgb
df = pd.read_csv('../input/PJME_hourly.csv', index_col=[0], parse_dates=[0])
import plotly.graph_objs as go
trace1 = go.Scatter(x=df.index, y=df.PJME_MW)
data = [trace1]
fig = dict(data=data)
splitdate = '2014-01-01'
df_train_set = df[df.index < splitdate]
df_test_set = df[df.index > splitdate]
trace1 = go.Scatter(x=df_train_set.index, y=df_train_set.PJME_MW)
data = [trace1]
fig = dict(data=data)
trace1 = go.Scatter(x=df_test_set.index, y=df_test_set.PJME_MW)
data = [trace1]
fig = dict(data=data)
def AddDateProperties(df):
df['date'] = df.index
df['hour'] = df['date'].dt.hour
df['dayofweek'] = df['date'].dt.dayofweek
df['quarter'] = df['date'].dt.quarter
df['month'] = df['date'].dt.month
df['year'] = df['date'].dt.year
df['dayofyear'] = df['date'].dt.dayofyear
df['dayofmonth'] = df['date'].dt.day
df['weekofyear'] = df['date'].dt.weekofyear
return df
df_test_set = AddDateProperties(df_test_set)
df_train_set = AddDateProperties(df_train_set)
df_test_set = df_test_set.drop(['date'], axis=1)
df_train_set = df_train_set.drop(['date'], axis=1)
y_test = df_test_set['PJME_MW']
y_train = df_train_set['PJME_MW']
X_test = df_test_set.loc[:, df_test_set.columns != 'PJME_MW']
X_train = df_train_set.loc[:, df_train_set.columns != 'PJME_MW']
model = xgb.XGBRegressor(learning_rate=0.01, n_estimators=1000, max_depth=3, subsample=0.8, colsample_bylevel=1)
model.fit(X_train, y_train, eval_set=[(X_train, y_train), (X_test, y_test)], early_stopping_rounds=50, verbose=False)
df_test_set['MW_Prediction'] = model.predict(X_test)
df_all = pd.concat([df_test_set, df_train_set], sort=False)
start_date ='2014-01-01'
end_date = '2014-01-31'
fig,ax = plt.subplots(1)
df_all[['PJME_MW' , 'MW_Prediction']].plot(ax=ax , figsize = (15,5 ) , style = ['.']);
ax.set_xbound(lower=start_date , upper= end_date)
start_date ='2015-02-20 00:00:00'
end_date = '2015-02-20 23:00:00'
fig,ax = plt.subplots(1)
df_all[['PJME_MW' , 'MW_Prediction']].plot(ax=ax , figsize = (15,5 ) , style = ['.']);
ax.set_xbound(lower=start_date , upper= end_date)
plot_tree(model, num_trees=1, rankdir='LR')
plt.show()
plt.rcParams['figure.figsize'] = (100, 70) | code |
16123290/cell_28 | [
"image_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import numpy as np
import pandas as pd
import plotly.graph_objs as go
import plotly.graph_objs as go
df = pd.read_csv('../input/PJME_hourly.csv', index_col=[0], parse_dates=[0])
import plotly.graph_objs as go
trace1 = go.Scatter(x=df.index, y=df.PJME_MW)
data = [trace1]
fig = dict(data=data)
splitdate = '2014-01-01'
df_train_set = df[df.index < splitdate]
df_test_set = df[df.index > splitdate]
trace1 = go.Scatter(x=df_train_set.index, y=df_train_set.PJME_MW)
data = [trace1]
fig = dict(data=data)
trace1 = go.Scatter(x=df_test_set.index, y=df_test_set.PJME_MW)
data = [trace1]
fig = dict(data=data)
def AddDateProperties(df):
df['date'] = df.index
df['hour'] = df['date'].dt.hour
df['dayofweek'] = df['date'].dt.dayofweek
df['quarter'] = df['date'].dt.quarter
df['month'] = df['date'].dt.month
df['year'] = df['date'].dt.year
df['dayofyear'] = df['date'].dt.dayofyear
df['dayofmonth'] = df['date'].dt.day
df['weekofyear'] = df['date'].dt.weekofyear
return df
df_test_set = AddDateProperties(df_test_set)
df_train_set = AddDateProperties(df_train_set)
df_test_set = df_test_set.drop(['date'], axis=1)
df_train_set = df_train_set.drop(['date'], axis=1)
y_test = df_test_set['PJME_MW']
y_train = df_train_set['PJME_MW']
X_test = df_test_set.loc[:, df_test_set.columns != 'PJME_MW']
X_train = df_train_set.loc[:, df_train_set.columns != 'PJME_MW']
df_test_set['AbsError'] = df_test_set.Error.apply(np.abs)
day_groupby = df_test_set.groupby(['year', 'month', 'dayofmonth'])
error_by_day = day_groupby['PJME_MW', 'MW_Prediction', 'Error', 'AbsError'].mean()
error_by_day.sort_values(ascending=True, by='AbsError').head(15)
error_by_day.sort_values(ascending=False, by='AbsError').head(15) | code |
16123290/cell_8 | [
"text_html_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import plotly.graph_objs as go
import plotly.graph_objs as go
df = pd.read_csv('../input/PJME_hourly.csv', index_col=[0], parse_dates=[0])
import plotly.graph_objs as go
trace1 = go.Scatter(x=df.index, y=df.PJME_MW)
data = [trace1]
fig = dict(data=data)
splitdate = '2014-01-01'
df_train_set = df[df.index < splitdate]
df_test_set = df[df.index > splitdate]
trace1 = go.Scatter(x=df_train_set.index, y=df_train_set.PJME_MW)
data = [trace1]
fig = dict(data=data)
iplot(fig) | code |
16123290/cell_15 | [
"text_html_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
from xgboost import plot_importance, plot_tree
import pandas as pd
import plotly.graph_objs as go
import plotly.graph_objs as go
import xgboost as xgb
df = pd.read_csv('../input/PJME_hourly.csv', index_col=[0], parse_dates=[0])
import plotly.graph_objs as go
trace1 = go.Scatter(x=df.index, y=df.PJME_MW)
data = [trace1]
fig = dict(data=data)
splitdate = '2014-01-01'
df_train_set = df[df.index < splitdate]
df_test_set = df[df.index > splitdate]
trace1 = go.Scatter(x=df_train_set.index, y=df_train_set.PJME_MW)
data = [trace1]
fig = dict(data=data)
trace1 = go.Scatter(x=df_test_set.index, y=df_test_set.PJME_MW)
data = [trace1]
fig = dict(data=data)
def AddDateProperties(df):
df['date'] = df.index
df['hour'] = df['date'].dt.hour
df['dayofweek'] = df['date'].dt.dayofweek
df['quarter'] = df['date'].dt.quarter
df['month'] = df['date'].dt.month
df['year'] = df['date'].dt.year
df['dayofyear'] = df['date'].dt.dayofyear
df['dayofmonth'] = df['date'].dt.day
df['weekofyear'] = df['date'].dt.weekofyear
return df
df_test_set = AddDateProperties(df_test_set)
df_train_set = AddDateProperties(df_train_set)
df_test_set = df_test_set.drop(['date'], axis=1)
df_train_set = df_train_set.drop(['date'], axis=1)
y_test = df_test_set['PJME_MW']
y_train = df_train_set['PJME_MW']
X_test = df_test_set.loc[:, df_test_set.columns != 'PJME_MW']
X_train = df_train_set.loc[:, df_train_set.columns != 'PJME_MW']
model = xgb.XGBRegressor(learning_rate=0.01, n_estimators=1000, max_depth=3, subsample=0.8, colsample_bylevel=1)
model.fit(X_train, y_train, eval_set=[(X_train, y_train), (X_test, y_test)], early_stopping_rounds=50, verbose=False)
plot_importance(model) | code |
16123290/cell_3 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/PJME_hourly.csv', index_col=[0], parse_dates=[0])
df.plot(figsize=(15, 8)) | code |
16123290/cell_31 | [
"text_html_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import numpy as np
import pandas as pd
import plotly.graph_objs as go
import plotly.graph_objs as go
df = pd.read_csv('../input/PJME_hourly.csv', index_col=[0], parse_dates=[0])
import plotly.graph_objs as go
trace1 = go.Scatter(x=df.index, y=df.PJME_MW)
data = [trace1]
fig = dict(data=data)
splitdate = '2014-01-01'
df_train_set = df[df.index < splitdate]
df_test_set = df[df.index > splitdate]
trace1 = go.Scatter(x=df_train_set.index, y=df_train_set.PJME_MW)
data = [trace1]
fig = dict(data=data)
trace1 = go.Scatter(x=df_test_set.index, y=df_test_set.PJME_MW)
data = [trace1]
fig = dict(data=data)
def AddDateProperties(df):
df['date'] = df.index
df['hour'] = df['date'].dt.hour
df['dayofweek'] = df['date'].dt.dayofweek
df['quarter'] = df['date'].dt.quarter
df['month'] = df['date'].dt.month
df['year'] = df['date'].dt.year
df['dayofyear'] = df['date'].dt.dayofyear
df['dayofmonth'] = df['date'].dt.day
df['weekofyear'] = df['date'].dt.weekofyear
return df
df_test_set = AddDateProperties(df_test_set)
df_train_set = AddDateProperties(df_train_set)
df_test_set = df_test_set.drop(['date'], axis=1)
df_train_set = df_train_set.drop(['date'], axis=1)
y_test = df_test_set['PJME_MW']
y_train = df_train_set['PJME_MW']
X_test = df_test_set.loc[:, df_test_set.columns != 'PJME_MW']
X_train = df_train_set.loc[:, df_train_set.columns != 'PJME_MW']
df_test_set['AbsError'] = df_test_set.Error.apply(np.abs)
day_groupby = df_test_set.groupby(['year', 'month', 'dayofmonth'])
error_by_day = day_groupby['PJME_MW', 'MW_Prediction', 'Error', 'AbsError'].mean()
error_by_day.sort_values(ascending=True, by='AbsError').head(15)
error_by_day.sort_values(ascending=False, by='AbsError').head(15)
error_by_day.sort_values(ascending=True, by='Error').head(15) | code |
16123290/cell_14 | [
"text_html_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import plotly.graph_objs as go
import plotly.graph_objs as go
import xgboost as xgb
df = pd.read_csv('../input/PJME_hourly.csv', index_col=[0], parse_dates=[0])
import plotly.graph_objs as go
trace1 = go.Scatter(x=df.index, y=df.PJME_MW)
data = [trace1]
fig = dict(data=data)
splitdate = '2014-01-01'
df_train_set = df[df.index < splitdate]
df_test_set = df[df.index > splitdate]
trace1 = go.Scatter(x=df_train_set.index, y=df_train_set.PJME_MW)
data = [trace1]
fig = dict(data=data)
trace1 = go.Scatter(x=df_test_set.index, y=df_test_set.PJME_MW)
data = [trace1]
fig = dict(data=data)
def AddDateProperties(df):
df['date'] = df.index
df['hour'] = df['date'].dt.hour
df['dayofweek'] = df['date'].dt.dayofweek
df['quarter'] = df['date'].dt.quarter
df['month'] = df['date'].dt.month
df['year'] = df['date'].dt.year
df['dayofyear'] = df['date'].dt.dayofyear
df['dayofmonth'] = df['date'].dt.day
df['weekofyear'] = df['date'].dt.weekofyear
return df
df_test_set = AddDateProperties(df_test_set)
df_train_set = AddDateProperties(df_train_set)
df_test_set = df_test_set.drop(['date'], axis=1)
df_train_set = df_train_set.drop(['date'], axis=1)
y_test = df_test_set['PJME_MW']
y_train = df_train_set['PJME_MW']
X_test = df_test_set.loc[:, df_test_set.columns != 'PJME_MW']
X_train = df_train_set.loc[:, df_train_set.columns != 'PJME_MW']
model = xgb.XGBRegressor(learning_rate=0.01, n_estimators=1000, max_depth=3, subsample=0.8, colsample_bylevel=1)
model.fit(X_train, y_train, eval_set=[(X_train, y_train), (X_test, y_test)], early_stopping_rounds=50, verbose=False) | code |
73070243/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data[['Pclass', 'Survived']].groupby('Pclass').mean() | code |
73070243/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
test_data.head() | code |
73070243/cell_57 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
concatenate = [train_data, test_data]
train_data = train_data.drop(['PassengerId', 'Ticket', 'Cabin'], axis=1)
test_data = test_data.drop(['Ticket', 'Cabin'], axis=1)
concatenate = [train_data, test_data]
category = {'Mr': 1, 'Mrs': 2, 'Miss': 3, 'Master': 4, 'uncommon': 5}
for dataset in concatenate:
dataset['Title'] = dataset['Title'].map(category)
train_data = train_data.drop('Name', axis=1)
test_data = test_data.drop('Name', axis=1)
concatenate = [train_data, test_data]
for dataset in concatenate:
dataset['Sex'] = dataset['Sex'].map({'female': 1, 'male': 0}).astype(int)
guess = np.zeros(5)
guess
for dataset in concatenate:
for i in range(1, 6):
dataset.loc[dataset['Age'].isnull() & (dataset['Title'] == i), 'Age'] = guess[i - 1]
dataset['Age'] = dataset['Age'].astype(int)
for dataset in concatenate:
dataset.loc[dataset['Age'] <= 16, 'Age'] = 1
dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'Age'] = 2
dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'Age'] = 3
dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'Age'] = 4
dataset.loc[dataset['Age'] > 64, 'Age'] = 5
train_data = train_data.drop('Age_Group', axis=1)
concatenate = [train_data, test_data]
for dataset in concatenate:
dataset['IsAlone'] = 0
dataset.loc[dataset['Family'] == 1, 'IsAlone'] = 1
train_data[['IsAlone', 'Survived']].groupby(['IsAlone']).mean() | code |
73070243/cell_33 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
concatenate = [train_data, test_data]
train_data = train_data.drop(['PassengerId', 'Ticket', 'Cabin'], axis=1)
test_data = test_data.drop(['Ticket', 'Cabin'], axis=1)
concatenate = [train_data, test_data]
for dataset in concatenate:
dataset['Title'] = dataset['Name'].str.extract('([A-Za-z]+)\\.', expand=False)
pd.crosstab(train_data['Title'], train_data['Sex']) | code |
73070243/cell_44 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
concatenate = [train_data, test_data]
train_data = train_data.drop(['PassengerId', 'Ticket', 'Cabin'], axis=1)
test_data = test_data.drop(['Ticket', 'Cabin'], axis=1)
concatenate = [train_data, test_data]
category = {'Mr': 1, 'Mrs': 2, 'Miss': 3, 'Master': 4, 'uncommon': 5}
for dataset in concatenate:
dataset['Title'] = dataset['Title'].map(category)
train_data = train_data.drop('Name', axis=1)
test_data = test_data.drop('Name', axis=1)
concatenate = [train_data, test_data]
for dataset in concatenate:
dataset['Sex'] = dataset['Sex'].map({'female': 1, 'male': 0}).astype(int)
train_data.head() | code |
73070243/cell_20 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
v = sns.FacetGrid(train_data, col='Survived')
v.map(plt.hist, 'Age', bins=10, color='red') | code |
73070243/cell_55 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
concatenate = [train_data, test_data]
train_data = train_data.drop(['PassengerId', 'Ticket', 'Cabin'], axis=1)
test_data = test_data.drop(['Ticket', 'Cabin'], axis=1)
concatenate = [train_data, test_data]
category = {'Mr': 1, 'Mrs': 2, 'Miss': 3, 'Master': 4, 'uncommon': 5}
for dataset in concatenate:
dataset['Title'] = dataset['Title'].map(category)
train_data = train_data.drop('Name', axis=1)
test_data = test_data.drop('Name', axis=1)
concatenate = [train_data, test_data]
for dataset in concatenate:
dataset['Sex'] = dataset['Sex'].map({'female': 1, 'male': 0}).astype(int)
guess = np.zeros(5)
guess
for dataset in concatenate:
for i in range(1, 6):
dataset.loc[dataset['Age'].isnull() & (dataset['Title'] == i), 'Age'] = guess[i - 1]
dataset['Age'] = dataset['Age'].astype(int)
for dataset in concatenate:
dataset.loc[dataset['Age'] <= 16, 'Age'] = 1
dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'Age'] = 2
dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'Age'] = 3
dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'Age'] = 4
dataset.loc[dataset['Age'] > 64, 'Age'] = 5
train_data = train_data.drop('Age_Group', axis=1)
concatenate = [train_data, test_data]
for dataset in concatenate:
dataset['Family'] = dataset['SibSp'] + dataset['Parch'] + 1
train_data[['Family', 'Survived']].groupby(['Family']).mean().sort_values(by='Survived', ascending=False) | code |
73070243/cell_39 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
concatenate = [train_data, test_data]
train_data = train_data.drop(['PassengerId', 'Ticket', 'Cabin'], axis=1)
test_data = test_data.drop(['Ticket', 'Cabin'], axis=1)
concatenate = [train_data, test_data]
category = {'Mr': 1, 'Mrs': 2, 'Miss': 3, 'Master': 4, 'uncommon': 5}
for dataset in concatenate:
dataset['Title'] = dataset['Title'].map(category)
train_data = train_data.drop('Name', axis=1)
test_data = test_data.drop('Name', axis=1)
train_data.head() | code |
73070243/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
v = sns.FacetGrid(train_data, col='Survived')
v.map(plt.hist, 'Age', bins=10, color='red')
v = sns.FacetGrid(train_data, col='Survived', row='Pclass')
v.map(plt.hist, 'Age', bins=10, color='green')
v.add_legend()
v = sns.FacetGrid(train_data, col='Survived', row='Pclass')
v.map(sns.scatterplot, 'Age', 'Sex', color='orange')
v.add_legend()
v = sns.FacetGrid(train_data, row='Embarked')
v.map(sns.pointplot, 'Pclass', 'Survived', 'Sex')
v.add_legend() | code |
73070243/cell_48 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
concatenate = [train_data, test_data]
train_data = train_data.drop(['PassengerId', 'Ticket', 'Cabin'], axis=1)
test_data = test_data.drop(['Ticket', 'Cabin'], axis=1)
concatenate = [train_data, test_data]
category = {'Mr': 1, 'Mrs': 2, 'Miss': 3, 'Master': 4, 'uncommon': 5}
for dataset in concatenate:
dataset['Title'] = dataset['Title'].map(category)
train_data = train_data.drop('Name', axis=1)
test_data = test_data.drop('Name', axis=1)
concatenate = [train_data, test_data]
guess = np.zeros(5)
guess
for dataset in concatenate:
for i in range(1, 6):
dataset.loc[dataset['Age'].isnull() & (dataset['Title'] == i), 'Age'] = guess[i - 1]
print(guess[i - 1])
dataset['Age'] = dataset['Age'].astype(int) | code |
73070243/cell_41 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
concatenate = [train_data, test_data]
train_data = train_data.drop(['PassengerId', 'Ticket', 'Cabin'], axis=1)
test_data = test_data.drop(['Ticket', 'Cabin'], axis=1)
concatenate = [train_data, test_data]
category = {'Mr': 1, 'Mrs': 2, 'Miss': 3, 'Master': 4, 'uncommon': 5}
for dataset in concatenate:
dataset['Title'] = dataset['Title'].map(category)
train_data = train_data.drop('Name', axis=1)
test_data = test_data.drop('Name', axis=1)
train_data.info() | code |
73070243/cell_50 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
concatenate = [train_data, test_data]
train_data = train_data.drop(['PassengerId', 'Ticket', 'Cabin'], axis=1)
test_data = test_data.drop(['Ticket', 'Cabin'], axis=1)
concatenate = [train_data, test_data]
for dataset in concatenate:
dataset['Title'] = dataset['Name'].str.extract('([A-Za-z]+)\\.', expand=False)
pd.crosstab(train_data['Title'], train_data['Sex'])
category = {'Mr': 1, 'Mrs': 2, 'Miss': 3, 'Master': 4, 'uncommon': 5}
for dataset in concatenate:
dataset['Title'] = dataset['Title'].map(category)
train_data = train_data.drop('Name', axis=1)
test_data = test_data.drop('Name', axis=1)
concatenate = [train_data, test_data]
for dataset in concatenate:
dataset['Sex'] = dataset['Sex'].map({'female': 1, 'male': 0}).astype(int)
train_data['Age_Group'] = pd.cut(train_data['Age'], 5)
train_data[['Age_Group', 'Survived']].groupby('Age_Group').mean() | code |
73070243/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
73070243/cell_45 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
guess = np.zeros(5)
guess | code |
73070243/cell_51 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
concatenate = [train_data, test_data]
train_data = train_data.drop(['PassengerId', 'Ticket', 'Cabin'], axis=1)
test_data = test_data.drop(['Ticket', 'Cabin'], axis=1)
concatenate = [train_data, test_data]
category = {'Mr': 1, 'Mrs': 2, 'Miss': 3, 'Master': 4, 'uncommon': 5}
for dataset in concatenate:
dataset['Title'] = dataset['Title'].map(category)
train_data = train_data.drop('Name', axis=1)
test_data = test_data.drop('Name', axis=1)
concatenate = [train_data, test_data]
for dataset in concatenate:
dataset['Sex'] = dataset['Sex'].map({'female': 1, 'male': 0}).astype(int)
guess = np.zeros(5)
guess
for dataset in concatenate:
for i in range(1, 6):
dataset.loc[dataset['Age'].isnull() & (dataset['Title'] == i), 'Age'] = guess[i - 1]
dataset['Age'] = dataset['Age'].astype(int)
for dataset in concatenate:
dataset.loc[dataset['Age'] <= 16, 'Age'] = 1
dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'Age'] = 2
dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'Age'] = 3
dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'Age'] = 4
dataset.loc[dataset['Age'] > 64, 'Age'] = 5
train_data.head() | code |
73070243/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
v = sns.FacetGrid(train_data, col='Survived')
v.map(plt.hist, 'Age', bins=10, color='red')
v = sns.FacetGrid(train_data, col='Survived', row='Pclass')
v.map(plt.hist, 'Age', bins=10, color='green')
v.add_legend()
v = sns.FacetGrid(train_data, col='Survived', row='Pclass')
v.map(sns.scatterplot, 'Age', 'Sex', color='orange')
v.add_legend()
v = sns.FacetGrid(train_data, row='Embarked')
v.map(sns.pointplot, 'Pclass', 'Survived', 'Sex')
v.add_legend()
v = sns.FacetGrid(train_data, col='Survived')
v.map(sns.barplot, 'Fare')
v.add_legend() | code |
73070243/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
print(train_data.columns)
print(train_data.info())
print(test_data.info()) | code |
73070243/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data[['Sex', 'Survived']].groupby('Sex').mean() | code |
73070243/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data[['SibSp', 'Survived']].groupby('SibSp').mean().sort_values(by='Survived', ascending=False) | code |
73070243/cell_47 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
concatenate = [train_data, test_data]
train_data = train_data.drop(['PassengerId', 'Ticket', 'Cabin'], axis=1)
test_data = test_data.drop(['Ticket', 'Cabin'], axis=1)
concatenate = [train_data, test_data]
category = {'Mr': 1, 'Mrs': 2, 'Miss': 3, 'Master': 4, 'uncommon': 5}
for dataset in concatenate:
dataset['Title'] = dataset['Title'].map(category)
train_data = train_data.drop('Name', axis=1)
test_data = test_data.drop('Name', axis=1)
concatenate = [train_data, test_data]
guess = np.zeros(5)
guess
for dataset in concatenate:
for i in range(1, 6):
gg = dataset[dataset['Title'] == i]['Age'].dropna()
ages = gg.median()
guess[i - 1] = ages
print(guess) | code |
73070243/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
train_data.head() | code |
73070243/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data[['Parch', 'Survived']].groupby('Parch').mean().sort_values(by='Survived', ascending=False) | code |
73070243/cell_35 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
concatenate = [train_data, test_data]
train_data = train_data.drop(['PassengerId', 'Ticket', 'Cabin'], axis=1)
test_data = test_data.drop(['Ticket', 'Cabin'], axis=1)
concatenate = [train_data, test_data]
for dataset in concatenate:
dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')
dataset['Title'] = dataset['Title'].replace('Mme', 'Miss')
dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')
dataset['Title'] = dataset['Title'].replace('Sir', 'Mr')
dataset['Title'] = dataset['Title'].replace(['Col', 'Don', 'Rev', 'Dr', 'Major', 'Lady', 'Capt', 'Countess', 'Jonkheer', 'Dona'], 'uncommon')
train_data[['Title', 'Survived']].groupby('Title').mean().sort_values(by='Survived', ascending=False) | code |
73070243/cell_24 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
v = sns.FacetGrid(train_data, col='Survived')
v.map(plt.hist, 'Age', bins=10, color='red')
v = sns.FacetGrid(train_data, col='Survived', row='Pclass')
v.map(plt.hist, 'Age', bins=10, color='green')
v.add_legend()
v = sns.FacetGrid(train_data, col='Survived', row='Pclass')
v.map(sns.scatterplot, 'Age', 'Sex', color='orange')
v.add_legend() | code |
73070243/cell_22 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
v = sns.FacetGrid(train_data, col='Survived')
v.map(plt.hist, 'Age', bins=10, color='red')
v = sns.FacetGrid(train_data, col='Survived', row='Pclass')
v.map(plt.hist, 'Age', bins=10, color='green')
v.add_legend() | code |
73070243/cell_53 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
concatenate = [train_data, test_data]
train_data = train_data.drop(['PassengerId', 'Ticket', 'Cabin'], axis=1)
test_data = test_data.drop(['Ticket', 'Cabin'], axis=1)
concatenate = [train_data, test_data]
category = {'Mr': 1, 'Mrs': 2, 'Miss': 3, 'Master': 4, 'uncommon': 5}
for dataset in concatenate:
dataset['Title'] = dataset['Title'].map(category)
train_data = train_data.drop('Name', axis=1)
test_data = test_data.drop('Name', axis=1)
concatenate = [train_data, test_data]
for dataset in concatenate:
dataset['Sex'] = dataset['Sex'].map({'female': 1, 'male': 0}).astype(int)
guess = np.zeros(5)
guess
for dataset in concatenate:
for i in range(1, 6):
dataset.loc[dataset['Age'].isnull() & (dataset['Title'] == i), 'Age'] = guess[i - 1]
dataset['Age'] = dataset['Age'].astype(int)
for dataset in concatenate:
dataset.loc[dataset['Age'] <= 16, 'Age'] = 1
dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'Age'] = 2
dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'Age'] = 3
dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'Age'] = 4
dataset.loc[dataset['Age'] > 64, 'Age'] = 5
train_data = train_data.drop('Age_Group', axis=1)
concatenate = [train_data, test_data]
train_data.head() | code |
73070243/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.describe(include=['O']) | code |
73070243/cell_37 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
concatenate = [train_data, test_data]
train_data = train_data.drop(['PassengerId', 'Ticket', 'Cabin'], axis=1)
test_data = test_data.drop(['Ticket', 'Cabin'], axis=1)
concatenate = [train_data, test_data]
category = {'Mr': 1, 'Mrs': 2, 'Miss': 3, 'Master': 4, 'uncommon': 5}
for dataset in concatenate:
dataset['Title'] = dataset['Title'].map(category)
train_data.head() | code |
1005662/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from sklearn import tree
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1005662/cell_3 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
combine = [train_data, test_data]
print(train_data.columns.values)
print(train_data.head())
print(train_data.describe())
train_data.shape | code |
17136141/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
suicide_df = pd.read_csv('../input/master.csv')
suicide_df.isnull().any()
suicide_df = suicide_df.drop(['HDI for year', 'country-year', 'gdp_per_capita ($)'], axis=1)
min_year = min(suicide_df.year)
max_year = max(suicide_df.year)
print('Max year :', max_year)
print('Min year :', min_year) | code |
17136141/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
suicide_df = pd.read_csv('../input/master.csv')
suicide_df.isnull().any()
suicide_df = suicide_df.drop(['HDI for year', 'country-year', 'gdp_per_capita ($)'], axis=1)
min_year = min(suicide_df.year)
max_year = max(suicide_df.year)
suicide_df.plot(x='generation', y='suicides_no', linestyle='', marker='o') | code |
17136141/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
suicide_df = pd.read_csv('../input/master.csv')
suicide_df.info() | code |
17136141/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
suicide_df = pd.read_csv('../input/master.csv')
suicide_df.isnull().any()
suicide_df = suicide_df.drop(['HDI for year', 'country-year', 'gdp_per_capita ($)'], axis=1)
min_year = min(suicide_df.year)
max_year = max(suicide_df.year)
suicide_df.groupby('year')['suicides_no'].sum().plot(kind='bar', figsize=(15, 10), cmap='summer') | code |
17136141/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
suicide_df = pd.read_csv('../input/master.csv')
suicide_df.isnull().any() | code |
17136141/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
suicide_df = pd.read_csv('../input/master.csv')
suicide_df.isnull().any()
suicide_df = suicide_df.drop(['HDI for year', 'country-year', 'gdp_per_capita ($)'], axis=1)
min_year = min(suicide_df.year)
max_year = max(suicide_df.year)
suicide_df.groupby('age')['suicides_no'].sum().plot(kind='bar', cmap='rainbow') | code |
17136141/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
suicide_df = pd.read_csv('../input/master.csv')
suicide_df.isnull().any()
sns.catplot('country', 'population', hue='age', data=suicide_df) | code |
17136141/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
suicide_df = pd.read_csv('../input/master.csv')
suicide_df.isnull().any()
suicide_df = suicide_df.drop(['HDI for year', 'country-year', 'gdp_per_capita ($)'], axis=1)
min_year = min(suicide_df.year)
max_year = max(suicide_df.year)
suicide_df.groupby('sex')['suicides_no'].sum().plot(kind='bar', cmap='RdBu') | code |
17136141/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
suicide_df = pd.read_csv('../input/master.csv')
suicide_df.isnull().any()
suicide_df = suicide_df.drop(['HDI for year', 'country-year', 'gdp_per_capita ($)'], axis=1)
min_year = min(suicide_df.year)
max_year = max(suicide_df.year)
df = suicide_df[['country', 'suicides_no']]
df1 = df.groupby('country').sum()
df1 = df1.sort_values(by='suicides_no', ascending=False).reset_index()
df1 = df1.loc[df1['suicides_no'] > 1000]
df1.head() | code |
17136141/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
suicide_df = pd.read_csv('../input/master.csv')
suicide_df.head() | code |
17136141/cell_17 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
suicide_df = pd.read_csv('../input/master.csv')
suicide_df.isnull().any()
suicide_df = suicide_df.drop(['HDI for year', 'country-year', 'gdp_per_capita ($)'], axis=1)
min_year = min(suicide_df.year)
max_year = max(suicide_df.year)
df = suicide_df[['country', 'suicides_no']]
df1 = df.groupby('country').sum()
df1 = df1.sort_values(by='suicides_no', ascending=False).reset_index()
df1 = df1.loc[df1['suicides_no'] > 1000]
plt.figure(figsize=(15, 20))
sns.barplot(x='suicides_no', y='country', data=df1) | code |
17136141/cell_24 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
suicide_df = pd.read_csv('../input/master.csv')
suicide_df.isnull().any()
suicide_df = suicide_df.drop(['HDI for year', 'country-year', 'gdp_per_capita ($)'], axis=1)
min_year = min(suicide_df.year)
max_year = max(suicide_df.year)
df = suicide_df[['country', 'suicides_no']]
df1 = df.groupby('country').sum()
df1 = df1.sort_values(by='suicides_no', ascending=False).reset_index()
df1 = df1.loc[df1['suicides_no'] > 1000]
plt.figure(figsize=(10, 6))
sns.countplot(x='generation', hue='sex', data=suicide_df) | code |
17136141/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
suicide_df = pd.read_csv('../input/master.csv')
suicide_df.isnull().any()
suicide_df = suicide_df.drop(['HDI for year', 'country-year', 'gdp_per_capita ($)'], axis=1)
min_year = min(suicide_df.year)
max_year = max(suicide_df.year)
pop = suicide_df[['country', 'population', 'suicides_no']]
pop.head() | code |
17136141/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
suicide_df = pd.read_csv('../input/master.csv')
suicide_df.isnull().any()
suicide_df = suicide_df.drop(['HDI for year', 'country-year', 'gdp_per_capita ($)'], axis=1)
min_year = min(suicide_df.year)
max_year = max(suicide_df.year)
df = suicide_df[['country', 'suicides_no']]
df.head() | code |
17136141/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
suicide_df = pd.read_csv('../input/master.csv')
suicide_df.describe() | code |
73067872/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/titanic/train.csv')
df | code |
73067872/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/titanic/train.csv')
df.Survived | code |
73067872/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/titanic/train.csv')
df | code |
73067872/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/titanic/train.csv')
df.info() | code |
32062482/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
apple_mobility_df = pd.read_csv('../input/apple-mobility-trends-updated-daily/Apple_Mobility_2020-04-13.csv')
apple_mobility_df.drop('Unnamed: 0', axis=1, inplace=True)
geo_mask = apple_mobility_df['geo_type'] == 'country/region'
mobility_countries = apple_mobility_df[geo_mask]
mobility_cities = apple_mobility_df[~geo_mask]
def get_trans_count(df):
name = df['geo_type'].iloc[0]
return df['transportation_type'].value_counts().rename(str(name))
transport_types_count = pd.concat([get_trans_count(mobility_countries), get_trans_count(mobility_cities)], axis=1, sort=False)
transport_types_count | code |
32062482/cell_6 | [
"text_html_output_2.png"
] | import pandas as pd
import plotly.express as px
import pandas as pd
apple_mobility_df = pd.read_csv('../input/apple-mobility-trends-updated-daily/Apple_Mobility_2020-04-13.csv')
apple_mobility_df.drop('Unnamed: 0', axis=1, inplace=True)
geo_mask = apple_mobility_df['geo_type'] == 'country/region'
mobility_countries = apple_mobility_df[geo_mask]
mobility_cities = apple_mobility_df[~geo_mask]
mobility_countries_melted = mobility_countries.melt(id_vars=['geo_type', 'region', 'transportation_type', 'lat', 'lng', 'population'], var_name='Date', value_name='pct_of_baseline')
mobility_cities_melted = mobility_cities.melt(id_vars=['geo_type', 'region', 'transportation_type', 'lat', 'lng', 'population'], var_name='Date', value_name='pct_of_baseline')
import plotly.express as px
to_show = ['Atlanta', 'Athens']
df = mobility_cities_melted[mobility_cities_melted['region'].isin(to_show)]
fig = px.line(df, x='Date', y='pct_of_baseline', color='transportation_type', line_group='region', hover_name='region')
fig.show() | code |
32062482/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
apple_mobility_df = pd.read_csv('../input/apple-mobility-trends-updated-daily/Apple_Mobility_2020-04-13.csv')
apple_mobility_df.drop('Unnamed: 0', axis=1, inplace=True)
apple_mobility_df.head() | code |
32062482/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
apple_mobility_df = pd.read_csv('../input/apple-mobility-trends-updated-daily/Apple_Mobility_2020-04-13.csv')
apple_mobility_df.drop('Unnamed: 0', axis=1, inplace=True)
geo_mask = apple_mobility_df['geo_type'] == 'country/region'
mobility_countries = apple_mobility_df[geo_mask]
mobility_cities = apple_mobility_df[~geo_mask]
print('There are a total of {} countires and {} cities with provided mobility data.'.format(len(mobility_countries), len(mobility_cities))) | code |
32062482/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
apple_mobility_df = pd.read_csv('../input/apple-mobility-trends-updated-daily/Apple_Mobility_2020-04-13.csv')
apple_mobility_df.drop('Unnamed: 0', axis=1, inplace=True)
geo_mask = apple_mobility_df['geo_type'] == 'country/region'
mobility_countries = apple_mobility_df[geo_mask]
mobility_cities = apple_mobility_df[~geo_mask]
mobility_countries_melted = mobility_countries.melt(id_vars=['geo_type', 'region', 'transportation_type', 'lat', 'lng', 'population'], var_name='Date', value_name='pct_of_baseline')
mobility_cities_melted = mobility_cities.melt(id_vars=['geo_type', 'region', 'transportation_type', 'lat', 'lng', 'population'], var_name='Date', value_name='pct_of_baseline')
mobility_cities_melted.head() | code |
106211205/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/segment/Segmentation_dataset.csv')
data.dtypes
data.isnull().sum()
data.columns | code |
106211205/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/segment/Segmentation_dataset.csv')
data.dtypes
data.isnull().sum() | code |
106211205/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/segment/Segmentation_dataset.csv')
data | code |
106211205/cell_6 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/segment/Segmentation_dataset.csv')
data.describe() | code |
106211205/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/segment/Segmentation_dataset.csv')
data.dtypes
data.isnull().sum()
data.columns
columns = ['Age', 'Annual Income (k$)', 'Spending Score (1-100)']
sns.pairplot(data) | code |
106211205/cell_7 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/segment/Segmentation_dataset.csv')
data.dtypes | code |
106211205/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/segment/Segmentation_dataset.csv')
data.dtypes
data.isnull().sum()
data.columns
columns = ['Age', 'Annual Income (k$)', 'Spending Score (1-100)']
for i in columns:
plt.figure()
sns.kdeplot(data[i], hue=data['Gender'], shade=True) | code |
106211205/cell_15 | [
"text_plain_output_1.png"
] | columns = ['Age', 'Annual Income (k$)', 'Spending Score (1-100)']
columns | code |
106211205/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/segment/Segmentation_dataset.csv')
data.dtypes
data.isnull().sum()
data.columns
columns = ['Age', 'Annual Income (k$)', 'Spending Score (1-100)']
for i in columns:
plt.figure()
sns.distplot(data[i]) | code |
106211205/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/segment/Segmentation_dataset.csv')
data.dtypes
data.isnull().sum()
sns.distplot(data['Annual Income (k$)']) | code |
322326/cell_2 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
act_df = pd.read_csv('../input/act_train.csv', sep=',')
sns.countplot(x='outcome', data=act_df)
sns.plt.show() | code |
322326/cell_3 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
act_df = pd.read_csv('../input/act_train.csv', sep=',')
sns.countplot(x='activity_category', data=act_df, hue='outcome')
sns.plt.show() | code |
32068059/cell_4 | [
"text_plain_output_1.png"
] | !pip install scispacy
!pip install https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.2.4/en_core_sci_lg-0.2.4.tar.gz
!jupyter nbextension enable --py --sys-prefix widgetsnbextension | code |
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