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stringlengths 13
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sequencelengths 1
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|---|---|---|---|
50244608/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
employees = pd.read_csv('../input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.shape
employees.dtypes
employees.isnull().sum()
employees.duplicated().sum()
sns.set_style('darkgrid')
employees.drop(['EmployeeCount', 'StandardHours', 'Over18', 'EmployeeNumber'], axis=1, inplace=True)
left = employees[employees['Attrition'] == 1]
stayed = employees[employees['Attrition'] == 0]
stayed.describe() | code |
50244608/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
employees = pd.read_csv('../input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.shape
employees.dtypes
employees.isnull().sum()
employees.duplicated().sum()
sns.set_style('darkgrid')
employees.hist(bins=30, figsize=(20, 20)) | code |
50244608/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
employees = pd.read_csv('../input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.shape
employees.dtypes | code |
50244608/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
employees = pd.read_csv('../input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.shape
employees.dtypes
employees.isnull().sum()
employees.duplicated().sum() | code |
50244608/cell_5 | [
"image_output_1.png"
] | !pip install plotly==4.14.1 | code |
50244608/cell_36 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
employees = pd.read_csv('../input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.shape
employees.dtypes
employees.isnull().sum()
employees.duplicated().sum()
sns.set_style('darkgrid')
employees.drop(['EmployeeCount', 'StandardHours', 'Over18', 'EmployeeNumber'], axis=1, inplace=True)
correlations = employees.corr()
f, ax = plt.subplots(figsize = (20,20))
sns.heatmap(correlations, annot=True);
plt.figure(figsize=[25, 12])
sns.countplot(x='Age', hue='Attrition', data=employees, palette='seismic_r') | code |
17118616/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from gensim.models import KeyedVectors
from nltk.tokenize import RegexpTokenizer
import pandas as pd
import gensim
from gensim.models import Word2Vec
from gensim.models import KeyedVectors
import pandas as pd
from nltk.tokenize import RegexpTokenizer
forum_posts = pd.read_csv('../input/meta-kaggle/ForumMessages.csv')
sample_data = forum_posts.Message[:100].astype('str').tolist()
tokenizer = RegexpTokenizer('\\w+')
sample_data_tokenized = [w.lower() for w in sample_data]
sample_data_tokenized = [tokenizer.tokenize(i) for i in sample_data_tokenized]
model = KeyedVectors.load_word2vec_format('../input/word2vec-google/GoogleNews-vectors-negative300.bin', binary=True)
model.build_vocab(sample_data_tokenized, update=True)
model.train(sample_data_tokenized) | code |
128007844/cell_6 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql.functions import col , round
spark = SparkSession.builder.appName('SparkByExamples.com').getOrCreate()
df = spark.read.csv('/kaggle/input/students-exam-scores/Original_data_with_more_rows.csv', header=True)
df.createOrReplaceTempView('students_score')
df = df.withColumn('total_score', col('MathScore') + col('ReadingScore') + col('WritingScore'))
df = df.withColumn('Percentage', round(col('total_score') / 300 * 100, 2))
df.show() | code |
128007844/cell_1 | [
"text_plain_output_1.png"
] | pip install pyspark | code |
128007844/cell_7 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('SparkByExamples.com').getOrCreate()
df = spark.read.csv('/kaggle/input/students-exam-scores/Original_data_with_more_rows.csv', header=True)
df_1 = spark.read.csv('/kaggle/input/students-exam-scores/Expanded_data_with_more_features.csv', header=True)
df_1.show()
column_names = df_1.columns
print(column_names) | code |
128007844/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('SparkByExamples.com').getOrCreate() | code |
73072005/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/breast-cancer-detection/train.csv')
test = pd.read_csv('../input/breast-cancer-detection/test.csv')
train.columns = ['_'.join(col.split(' ')).lower() for col in train.columns]
test.columns = ['_'.join(col.split(' ')).lower() for col in test.columns]
a = train.columns
b = test.columns
train = train.drop('unnamed:_32', axis=1)
test = test.drop('unnamed:_32', axis=1)
print('Null values in Train dataset are as follows:\n')
a = train.isnull().sum()
print(a[a > 0])
print('\n', 'o' * 80, '\n')
print('Null values in Train dataset are as follows:\n')
b = test.isnull().sum()
print(b[b > 0]) | code |
73072005/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/breast-cancer-detection/train.csv')
test = pd.read_csv('../input/breast-cancer-detection/test.csv')
train.columns = ['_'.join(col.split(' ')).lower() for col in train.columns]
test.columns = ['_'.join(col.split(' ')).lower() for col in test.columns]
print('Columns in train dataset are as follows:\n')
a = train.columns
print(*a, sep=', ')
print('\n', '$' * 100, '\n')
print('Columns in test dataset are as follows:\n')
b = test.columns
print(*b, sep=', ') | code |
73072005/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
ss = StandardScaler()
X_train = ss.fit_transform(X_train)
X_valid = ss.transform(X_valid) | code |
73072005/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/breast-cancer-detection/train.csv')
test = pd.read_csv('../input/breast-cancer-detection/test.csv')
test.head() | code |
73072005/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
print('Priyatama is ready!') | code |
73072005/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/breast-cancer-detection/train.csv')
test = pd.read_csv('../input/breast-cancer-detection/test.csv')
train.columns = ['_'.join(col.split(' ')).lower() for col in train.columns]
test.columns = ['_'.join(col.split(' ')).lower() for col in test.columns]
a = train.columns
b = test.columns
train = train.drop('unnamed:_32', axis=1)
test = test.drop('unnamed:_32', axis=1)
print('Object type columns in Train dataset as follows:\n')
print(train.select_dtypes(exclude='float64'))
print('\n', '$' * 100, '\n')
print('Object type columns in Test dataset as follows:\n')
print(test.select_dtypes(exclude='float64')) | code |
73072005/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/breast-cancer-detection/train.csv')
test = pd.read_csv('../input/breast-cancer-detection/test.csv')
train.columns = ['_'.join(col.split(' ')).lower() for col in train.columns]
test.columns = ['_'.join(col.split(' ')).lower() for col in test.columns]
a = train.columns
b = test.columns
train = train.drop('unnamed:_32', axis=1)
test = test.drop('unnamed:_32', axis=1)
a = train.isnull().sum()
b = test.isnull().sum()
corr = train.corr()
fig, ax = plt.subplots(figsize=(7, 7))
sns.heatmap(corr, xticklabels=corr.columns.values, yticklabels=corr.columns.values) | code |
73072005/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/breast-cancer-detection/train.csv')
test = pd.read_csv('../input/breast-cancer-detection/test.csv')
train.columns = ['_'.join(col.split(' ')).lower() for col in train.columns]
test.columns = ['_'.join(col.split(' ')).lower() for col in test.columns]
a = train.columns
b = test.columns
train = train.drop('unnamed:_32', axis=1)
test = test.drop('unnamed:_32', axis=1)
train.diagnosis.value_counts().plot(kind='bar') | code |
73072005/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/breast-cancer-detection/train.csv')
test = pd.read_csv('../input/breast-cancer-detection/test.csv')
train.head() | code |
105199235/cell_11 | [
"text_plain_output_1.png"
] | !ls | code |
105199235/cell_10 | [
"text_plain_output_1.png"
] | !pip wheel --verbose --no-binary cython-bbox==0.1.3 cython-bbox -w /kaggle/working/
!pip wheel --verbose --no-binary lap==0.4.0 lap -w /kaggle/working/
!pip wheel --verbose --no-binary loguru-0.6.0 loguru -w /kaggle/working/
!pip wheel --verbose --no-binary thop-0.1.1.post2209072238 thop -w /kaggle/working/
!git clone https://github.com/ifzhang/ByteTrack.git
!cd ByteTrack && python3 setup.py bdist_wheel && cp -r ./dist/* /kaggle/working/
!rm -rf /kaggle/working/ByteTrack | code |
105199235/cell_12 | [
"text_plain_output_1.png"
] | !pip install cython_bbox-0.1.3-cp37-cp37m-linux_x86_64.whl
!pip install lap-0.4.0-cp37-cp37m-linux_x86_64.whl
!pip install yolox-0.1.0-cp37-cp37m-linux_x86_64.whl | code |
73096303/cell_21 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers.experimental.preprocessing import TextVectorization
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
tf.__version__
data = pd.read_csv('../input/womens-ecommerce-clothing-reviews/Womens Clothing E-Commerce Reviews.csv', index_col=0)
data.shape
data = data[~data['Review Text'].isnull()]
data.shape
labels = tf.keras.utils.to_categorical(data['Recommended IND'])
output_shape = labels.shape[1]
(labels, output_shape)
v_size = 2000
max_len = 100
e_dim = 64
batch_size = 256
pre_processing_layer = TextVectorization(max_tokens=v_size, output_sequence_length=max_len, name='Notes_preprocessing_layer')
pre_processing_layer.adapt(X_train)
vocab = pre_processing_layer.get_vocabulary()
tf.keras.backend.clear_session()
model = tf.keras.models.Sequential([tf.keras.layers.Embedding(input_dim=v_size, output_dim=e_dim, name='embedding', mask_zero=True), tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)), tf.keras.layers.Dense(16, activation='relu'), tf.keras.layers.Dense(output_shape, activation='softmax')])
metrics = [tf.keras.metrics.CategoricalAccuracy()]
model.summary()
model.compile(optimizer='adam', loss=tf.keras.losses.CategoricalCrossentropy(from_logits=False), metrics=metrics)
train_dataset = tf.data.Dataset.from_tensor_slices((X_train, y_train))
valid_dataset = tf.data.Dataset.from_tensor_slices((X_val, y_val))
test_dataset = tf.data.Dataset.from_tensor_slices((X_test, y_test))
options = tf.data.Options()
options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.DATA
raw_train_ds = train_dataset.shuffle(X_train.shape[0]).batch(batch_size).with_options(options)
raw_val_ds = valid_dataset.batch(batch_size).with_options(options)
raw_test_ds = test_dataset.batch(batch_size).with_options(options)
@tf.autograph.experimental.do_not_convert
def vectorize_text(text, label):
""" convert text to tokens """
text = tf.expand_dims(text, -1)
return (pre_processing_layer(text), label)
text_batch, label_batch = next(iter(raw_train_ds.shuffle(50)))
first_review, first_label = (text_batch[0], label_batch[0])
print('Review: ', first_review)
print('Label: ', tf.argmax(first_label))
print('Vectorized review', vectorize_text(first_review, first_label)) | code |
73096303/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/womens-ecommerce-clothing-reviews/Womens Clothing E-Commerce Reviews.csv', index_col=0)
data.shape
data = data[~data['Review Text'].isnull()]
data.shape
data['Recommended IND'].value_counts() | code |
73096303/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/womens-ecommerce-clothing-reviews/Womens Clothing E-Commerce Reviews.csv', index_col=0)
data.head() | code |
73096303/cell_23 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers.experimental.preprocessing import TextVectorization
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
tf.__version__
data = pd.read_csv('../input/womens-ecommerce-clothing-reviews/Womens Clothing E-Commerce Reviews.csv', index_col=0)
data.shape
data = data[~data['Review Text'].isnull()]
data.shape
labels = tf.keras.utils.to_categorical(data['Recommended IND'])
output_shape = labels.shape[1]
(labels, output_shape)
v_size = 2000
max_len = 100
e_dim = 64
batch_size = 256
pre_processing_layer = TextVectorization(max_tokens=v_size, output_sequence_length=max_len, name='Notes_preprocessing_layer')
pre_processing_layer.adapt(X_train)
vocab = pre_processing_layer.get_vocabulary()
tf.keras.backend.clear_session()
model = tf.keras.models.Sequential([tf.keras.layers.Embedding(input_dim=v_size, output_dim=e_dim, name='embedding', mask_zero=True), tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)), tf.keras.layers.Dense(16, activation='relu'), tf.keras.layers.Dense(output_shape, activation='softmax')])
metrics = [tf.keras.metrics.CategoricalAccuracy()]
model.summary()
model.compile(optimizer='adam', loss=tf.keras.losses.CategoricalCrossentropy(from_logits=False), metrics=metrics)
train_dataset = tf.data.Dataset.from_tensor_slices((X_train, y_train))
valid_dataset = tf.data.Dataset.from_tensor_slices((X_val, y_val))
test_dataset = tf.data.Dataset.from_tensor_slices((X_test, y_test))
options = tf.data.Options()
options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.DATA
raw_train_ds = train_dataset.shuffle(X_train.shape[0]).batch(batch_size).with_options(options)
raw_val_ds = valid_dataset.batch(batch_size).with_options(options)
raw_test_ds = test_dataset.batch(batch_size).with_options(options)
@tf.autograph.experimental.do_not_convert
def vectorize_text(text, label):
""" convert text to tokens """
text = tf.expand_dims(text, -1)
return (pre_processing_layer(text), label)
text_batch, label_batch = next(iter(raw_train_ds.shuffle(50)))
first_review, first_label = (text_batch[0], label_batch[0])
train_ds = raw_train_ds.map(vectorize_text)
val_ds = raw_val_ds.map(vectorize_text)
test_ds = raw_test_ds.map(vectorize_text)
AUTOTUNE = tf.data.AUTOTUNE
train_ds = train_ds.cache().prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)
test_ds = test_ds.cache().prefetch(buffer_size=AUTOTUNE)
model.fit(train_ds, validation_data=val_ds, epochs=3, verbose=1) | code |
73096303/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/womens-ecommerce-clothing-reviews/Womens Clothing E-Commerce Reviews.csv', index_col=0)
data.shape
data = data[~data['Review Text'].isnull()]
data.shape | code |
73096303/cell_2 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.__version__ | code |
73096303/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/womens-ecommerce-clothing-reviews/Womens Clothing E-Commerce Reviews.csv', index_col=0)
data.shape
data = data[~data['Review Text'].isnull()]
data.shape
X = data['Review Text'].values
X[:3] | code |
73096303/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
tf.__version__
data = pd.read_csv('../input/womens-ecommerce-clothing-reviews/Womens Clothing E-Commerce Reviews.csv', index_col=0)
data.shape
data = data[~data['Review Text'].isnull()]
data.shape
labels = tf.keras.utils.to_categorical(data['Recommended IND'])
output_shape = labels.shape[1]
(labels, output_shape)
v_size = 2000
max_len = 100
e_dim = 64
batch_size = 256
tf.keras.backend.clear_session()
model = tf.keras.models.Sequential([tf.keras.layers.Embedding(input_dim=v_size, output_dim=e_dim, name='embedding', mask_zero=True), tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)), tf.keras.layers.Dense(16, activation='relu'), tf.keras.layers.Dense(output_shape, activation='softmax')])
metrics = [tf.keras.metrics.CategoricalAccuracy()]
model.summary()
model.compile(optimizer='adam', loss=tf.keras.losses.CategoricalCrossentropy(from_logits=False), metrics=metrics)
print('Ready to Train') | code |
73096303/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras.layers.experimental.preprocessing import TextVectorization
from sklearn.model_selection import train_test_split
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib as mpl
from matplotlib.colors import Normalize, rgb2hex
from IPython.display import HTML
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
73096303/cell_7 | [
"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)
data = pd.read_csv('../input/womens-ecommerce-clothing-reviews/Womens Clothing E-Commerce Reviews.csv', index_col=0)
data.shape
data = data[~data['Review Text'].isnull()]
data.shape
data['Recommended IND'].isnull().sum() | code |
73096303/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
tf.__version__
data = pd.read_csv('../input/womens-ecommerce-clothing-reviews/Womens Clothing E-Commerce Reviews.csv', index_col=0)
data.shape
data = data[~data['Review Text'].isnull()]
data.shape
labels = tf.keras.utils.to_categorical(data['Recommended IND'])
output_shape = labels.shape[1]
(labels, output_shape)
v_size = 2000
max_len = 100
e_dim = 64
batch_size = 256
tf.keras.backend.clear_session()
model = tf.keras.models.Sequential([tf.keras.layers.Embedding(input_dim=v_size, output_dim=e_dim, name='embedding', mask_zero=True), tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)), tf.keras.layers.Dense(16, activation='relu'), tf.keras.layers.Dense(output_shape, activation='softmax')])
metrics = [tf.keras.metrics.CategoricalAccuracy()]
model.summary() | code |
73096303/cell_8 | [
"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)
data = pd.read_csv('../input/womens-ecommerce-clothing-reviews/Womens Clothing E-Commerce Reviews.csv', index_col=0)
data.shape
data = data[~data['Review Text'].isnull()]
data.shape
data['Review Text'].str.split().apply(lambda x: len(x)).describe() | code |
73096303/cell_24 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers.experimental.preprocessing import TextVectorization
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
tf.__version__
data = pd.read_csv('../input/womens-ecommerce-clothing-reviews/Womens Clothing E-Commerce Reviews.csv', index_col=0)
data.shape
data = data[~data['Review Text'].isnull()]
data.shape
labels = tf.keras.utils.to_categorical(data['Recommended IND'])
output_shape = labels.shape[1]
(labels, output_shape)
v_size = 2000
max_len = 100
e_dim = 64
batch_size = 256
pre_processing_layer = TextVectorization(max_tokens=v_size, output_sequence_length=max_len, name='Notes_preprocessing_layer')
pre_processing_layer.adapt(X_train)
vocab = pre_processing_layer.get_vocabulary()
tf.keras.backend.clear_session()
model = tf.keras.models.Sequential([tf.keras.layers.Embedding(input_dim=v_size, output_dim=e_dim, name='embedding', mask_zero=True), tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)), tf.keras.layers.Dense(16, activation='relu'), tf.keras.layers.Dense(output_shape, activation='softmax')])
metrics = [tf.keras.metrics.CategoricalAccuracy()]
model.summary()
model.compile(optimizer='adam', loss=tf.keras.losses.CategoricalCrossentropy(from_logits=False), metrics=metrics)
train_dataset = tf.data.Dataset.from_tensor_slices((X_train, y_train))
valid_dataset = tf.data.Dataset.from_tensor_slices((X_val, y_val))
test_dataset = tf.data.Dataset.from_tensor_slices((X_test, y_test))
options = tf.data.Options()
options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.DATA
raw_train_ds = train_dataset.shuffle(X_train.shape[0]).batch(batch_size).with_options(options)
raw_val_ds = valid_dataset.batch(batch_size).with_options(options)
raw_test_ds = test_dataset.batch(batch_size).with_options(options)
@tf.autograph.experimental.do_not_convert
def vectorize_text(text, label):
""" convert text to tokens """
text = tf.expand_dims(text, -1)
return (pre_processing_layer(text), label)
text_batch, label_batch = next(iter(raw_train_ds.shuffle(50)))
first_review, first_label = (text_batch[0], label_batch[0])
train_ds = raw_train_ds.map(vectorize_text)
val_ds = raw_val_ds.map(vectorize_text)
test_ds = raw_test_ds.map(vectorize_text)
AUTOTUNE = tf.data.AUTOTUNE
train_ds = train_ds.cache().prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)
test_ds = test_ds.cache().prefetch(buffer_size=AUTOTUNE)
model.fit(train_ds, validation_data=val_ds, epochs=3, verbose=1)
model.evaluate(test_ds) | code |
73096303/cell_14 | [
"text_plain_output_1.png"
] | print(X_train.shape, y_train.shape)
print(X_val.shape, y_val.shape)
print(X_test.shape, y_test.shape) | code |
73096303/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
tf.__version__
data = pd.read_csv('../input/womens-ecommerce-clothing-reviews/Womens Clothing E-Commerce Reviews.csv', index_col=0)
data.shape
data = data[~data['Review Text'].isnull()]
data.shape
labels = tf.keras.utils.to_categorical(data['Recommended IND'])
output_shape = labels.shape[1]
(labels, output_shape) | code |
73096303/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/womens-ecommerce-clothing-reviews/Womens Clothing E-Commerce Reviews.csv', index_col=0)
data.shape | code |
90142380/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | angka = int(input('Masukkan Bilangan Angka = '))
biner = bin(angka).replace('0b', '')
oktal = oct(angka).replace('0o', '')
hexa = hex(angka).replace('0x', '')
print(biner, oktal, hexa) | code |
2043499/cell_6 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from numba import jit
import math
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
gift_pref = pd.read_csv('../input/child_wishlist_v2.csv', header=None).drop(0, 1).values
child_pref = pd.read_csv('../input/gift_goodkids_v2.csv', header=None).drop(0, 1).values
random_sub = pd.read_csv('../input/sample_submission_random_v2.csv').values.tolist()
n_children = 1000000
n_gift_type = 1000
n_gift_quantity = 1000
n_gift_pref = 100
n_child_pref = 1000
twins = math.ceil(0.04 * n_children / 2.0) * 2
triplets = math.ceil(0.005 * n_children / 3.0) * 3
ratio_gift_happiness = 2
ratio_child_happiness = 2
@jit(nopython=True)
def gcd(x, y):
""" return greatest common divisor for two integers """
while y != 0:
x, y = (y, x % y)
return x
@jit(nopython=True)
def lcm(a, b):
"""Compute the lowest common multiple of a and b"""
return a * b // gcd(a, b)
@jit(nopython=True)
def anh(pred, child_pref, gift_pref):
n_children = 1000000
n_gift_type = 1000
n_gift_quantity = 1000
n_gift_pref = 100
n_child_pref = 1000
twins = math.ceil(0.04 * n_children / 2.0) * 2
triplets = math.ceil(0.005 * n_children / 3.0) * 3
ratio_gift_happiness = 2
ratio_child_happiness = 2
tmp_dict = np.zeros(n_gift_quantity, dtype=np.uint16)
for i in np.arange(len(pred)):
tmp_dict[pred[i][1]] += 1
for count in np.arange(n_gift_quantity):
assert count <= n_gift_quantity
for t1 in np.arange(0, triplets, 3):
triplet1 = pred[t1]
triplet2 = pred[t1 + 1]
triplet3 = pred[t1 + 2]
assert triplet1[1] == triplet2[1] and triplet2[1] == triplet3[1]
for t1 in np.arange(triplets, triplets + twins, 2):
twin1 = pred[t1]
twin2 = pred[t1 + 1]
assert twin1[1] == twin2[1]
max_child_happiness = n_gift_pref * ratio_child_happiness
max_gift_happiness = n_child_pref * ratio_gift_happiness
total_child_happiness = 0
total_gift_happiness = np.zeros(n_gift_type)
for i in np.arange(len(pred)):
row = pred[i]
child_id = row[0]
gift_id = row[1]
assert child_id < n_children
assert gift_id < n_gift_type
assert child_id >= 0
assert gift_id >= 0
if np.sum(gift_pref[child_id] == gift_id):
child_happiness = (n_gift_pref - np.where(gift_pref[child_id] == gift_id)[0]) * ratio_child_happiness
tmp_child_happiness = child_happiness[0]
else:
tmp_child_happiness = -1
if np.sum(child_pref[gift_id] == child_id):
gift_happiness = (n_child_pref - np.where(child_pref[gift_id] == child_id)[0]) * ratio_gift_happiness
tmp_gift_happiness = gift_happiness[0]
else:
tmp_gift_happiness = -1
total_child_happiness += tmp_child_happiness
total_gift_happiness[gift_id] += tmp_gift_happiness
denominator1 = n_children * max_child_happiness
denominator2 = n_gift_quantity * max_gift_happiness * n_gift_type
common_denom = lcm(denominator1, denominator2)
multiplier = common_denom / denominator1
return float(math.pow(total_child_happiness * multiplier, 3) + math.pow(np.sum(total_gift_happiness), 3)) / float(math.pow(common_denom, 3))
anh(np.array(random_sub), child_pref, gift_pref) | code |
2043499/cell_7 | [
"text_plain_output_1.png"
] | '''
INPUT_PATH = '../input/'
def lcm(a, b):
"""Compute the lowest common multiple of a and b"""
# in case of large numbers, using floor division
return a * b // math.gcd(a, b)
#from numba import jit
#@jit(nopython=True)
def avg_normalized_happiness(pred, gift, wish):
n_children = 1000000 # n children to give
n_gift_type = 1000 # n types of gifts available
n_gift_quantity = 1000 # each type of gifts are limited to this quantity
n_gift_pref = 100 # number of gifts a child ranks
n_child_pref = 1000 # number of children a gift ranks
twins = math.ceil(0.04 * n_children / 2.) * 2 # 4% of all population, rounded to the closest number
triplets = math.ceil(0.005 * n_children / 3.) * 3 # 0.5% of all population, rounded to the closest number
ratio_gift_happiness = 2
ratio_child_happiness = 2
# check if triplets have the same gift
for t1 in np.arange(0, triplets, 3):
triplet1 = pred[t1]
triplet2 = pred[t1+1]
triplet3 = pred[t1+2]
# print(t1, triplet1, triplet2, triplet3)
assert triplet1 == triplet2 and triplet2 == triplet3
# check if twins have the same gift
for t1 in np.arange(triplets, triplets+twins, 2):
twin1 = pred[t1]
twin2 = pred[t1+1]
# print(t1)
assert twin1 == twin2
max_child_happiness = n_gift_pref * ratio_child_happiness
max_gift_happiness = n_child_pref * ratio_gift_happiness
total_child_happiness = 1000
total_gift_happiness = np.zeros(n_gift_type)
for i in range(len(pred)):
child_id = i
gift_id = pred[i]
# check if child_id and gift_id exist
assert child_id < n_children
assert gift_id < n_gift_type
assert child_id >= 0
assert gift_id >= 0
child_happiness = (n_gift_pref - np.where(wish[child_id]==gift_id)[0]) * ratio_child_happiness
if not child_happiness:
child_happiness = -1
gift_happiness = ( n_child_pref - np.where(gift[gift_id]==child_id)[0]) * ratio_gift_happiness
if not gift_happiness:
gift_happiness = -1
total_child_happiness += child_happiness
total_gift_happiness[gift_id] += gift_happiness
denominator1 = n_children*max_child_happiness
denominator2 = n_gift_quantity*max_gift_happiness*n_gift_type
common_denom = lcm(denominator1, denominator2)
multiplier = common_denom / denominator1
ret = float(math.pow(total_child_happiness*multiplier,3) + math.pow(np.sum(total_gift_happiness),3)) / float(math.pow(common_denom,3))
return ret
def get_overall_hapiness(wish, gift):
res_child = dict()
for i in range(0, wish.shape[0]):
for j in range(55):
res_child[(i, wish[i][j])] = int(100* (1 + (wish.shape[1] - j)*2))
res_santa = dict()
for i in range(gift.shape[0]):
for j in range(gift.shape[1]):
res_santa[(gift[i][j], i)] = int((1 + (gift.shape[1] - j)*2))
positive_cases = list(set(res_santa.keys()) | set(res_child.keys()))
print('Positive case tuples (child, gift): {}'.format(len(positive_cases)))
res = dict()
for p in positive_cases:
res[p] = 0
if p in res_child:
res[p] += res_child[p]
if p in res_santa:
res[p] += res_santa[p]
return res
def sort_dict_by_values(a, reverse=True):
sorted_x = sorted(a.items(), key=operator.itemgetter(1), reverse=reverse)
return sorted_x
def value_counts_for_list(lst):
a = dict(Counter(lst))
a = sort_dict_by_values(a, True)
return a
def get_most_desired_gifts(wish, gift):
best_gifts = value_counts_for_list(np.ravel(wish))
return best_gifts
def recalc_hapiness(happiness, best_gifts, gift):
recalc = dict()
for b in best_gifts:
recalc[b[0]] = b[1] / 2000000
for h in happiness:
c, g = h
happiness[h] /= recalc[g]
# Make triples/twins more happy
# if c <= 45000 and happiness[h] < 0.00001:
# happiness[h] = 0.00001
return happiness
def solve():
wish = pd.read_csv(INPUT_PATH + 'child_wishlist_v2.csv', header=None).as_matrix()[:, 1:]
gift_init = pd.read_csv(INPUT_PATH + 'gift_goodkids_v2.csv', header=None).as_matrix()[:, 1:]
gift = gift_init.copy()
answ = np.zeros(len(wish), dtype=np.int32)
answ[:] = -1
gift_count = np.zeros(len(gift), dtype=np.int32)
happiness = get_overall_hapiness(wish, gift)
best_gifts = get_most_desired_gifts(wish, gift)
happiness = recalc_hapiness(happiness, best_gifts, gift)
sorted_hapiness = sort_dict_by_values(happiness)
print('Happiness sorted...')
for i in range(len(sorted_hapiness)):
child = sorted_hapiness[i][0][0]
g = sorted_hapiness[i][0][1]
if answ[child] != -1:
continue
if gift_count[g] >= 1000:
continue
if child <= 5000 and gift_count[g] < 997:
if child % 3 == 0:
answ[child] = g
answ[child+1] = g
answ[child+2] = g
gift_count[g] += 3
elif child % 3 == 1:
answ[child] = g
answ[child-1] = g
answ[child+1] = g
gift_count[g] += 3
else:
answ[child] = g
answ[child-1] = g
answ[child-2] = g
gift_count[g] += 3
elif child > 5000 and child <= 45000 and gift_count[g] < 998:
if child % 2 == 0:
answ[child] = g
answ[child - 1] = g
gift_count[g] += 2
else:
answ[child] = g
answ[child + 1] = g
gift_count[g] += 2
elif child > 45000:
answ[child] = g
gift_count[g] += 1
print('Left unhappy:', len(answ[answ == -1]))
# unhappy children
for child in range(45001, len(answ)):
if answ[child] == -1:
g = np.argmin(gift_count)
answ[child] = g
gift_count[g] += 1
if answ.min() == -1:
print('Some children without present')
exit()
if gift_count.max() > 1000:
print('Some error in kernel: {}'.format(gift_count.max()))
exit()
print('Start score calculation...')
# score = avg_normalized_happiness(answ, gift_init, wish)
# print('Predicted score: {:.8f}'.format(score))
score = avg_normalized_happiness(answ, gift, wish)
print('Predicted score: {:.8f}'.format(score))
out = open('subm_{}.csv'.format(score), 'w')
out.write('ChildId,GiftId
')
for i in range(len(answ)):
out.write(str(i) + ',' + str(answ[i]) + '
')
out.close()
solve()
''' | code |
16110432/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/Mall_Customers.csv')
dataset.rename(columns={'Annual Income (k$)': 'Annual_Income', 'Spending Score (1-100)': 'Spending_Score'}, inplace=True)
dataset.shape | code |
16110432/cell_25 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
dataset = pd.read_csv('../input/Mall_Customers.csv')
dataset.rename(columns={'Annual Income (k$)': 'Annual_Income', 'Spending Score (1-100)': 'Spending_Score'}, inplace=True)
dataset.shape
dataset.nunique()
plt.figure(figsize=(8, 8))
colors = ['LightBlue', 'Lightgreen']
explode = [0, 0.1]
plt.pie(dataset['Gender'].value_counts(), explode=explode, colors=colors, autopct='%1.1f%%', shadow=True, startangle=140)
plt.legend(labels=['Female', 'Male'])
plt.title('Male v/s Female Distribution')
plt.axis('off') | code |
16110432/cell_23 | [
"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
dataset = pd.read_csv('../input/Mall_Customers.csv')
dataset.rename(columns={'Annual Income (k$)': 'Annual_Income', 'Spending Score (1-100)': 'Spending_Score'}, inplace=True)
dataset.shape
dataset.nunique()
plt.figure(figsize=(20, 8))
sns.countplot(dataset['Spending_Score'])
plt.title('Spending Score Distribution')
plt.xlabel('Spending Score')
plt.ylabel('Count')
plt.show() | code |
16110432/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
dataset = pd.read_csv('../input/Mall_Customers.csv')
dataset.rename(columns={'Annual Income (k$)': 'Annual_Income', 'Spending Score (1-100)': 'Spending_Score'}, inplace=True)
dataset.shape
dataset.nunique()
plt.figure(figsize=(15, 5))
sns.countplot(dataset['Age'])
plt.title('Age Distribution')
plt.xlabel('Age')
plt.show() | code |
16110432/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/Mall_Customers.csv')
dataset.rename(columns={'Annual Income (k$)': 'Annual_Income', 'Spending Score (1-100)': 'Spending_Score'}, inplace=True)
dataset.shape
dataset.nunique()
dataset['Gender'].value_counts() | code |
16110432/cell_2 | [
"text_html_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
print(os.listdir('../input')) | code |
16110432/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/Mall_Customers.csv')
dataset.rename(columns={'Annual Income (k$)': 'Annual_Income', 'Spending Score (1-100)': 'Spending_Score'}, inplace=True)
dataset.shape
dataset.head() | code |
16110432/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/Mall_Customers.csv')
dataset.rename(columns={'Annual Income (k$)': 'Annual_Income', 'Spending Score (1-100)': 'Spending_Score'}, inplace=True)
dataset.shape
dataset.info() | code |
16110432/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/Mall_Customers.csv')
dataset.rename(columns={'Annual Income (k$)': 'Annual_Income', 'Spending Score (1-100)': 'Spending_Score'}, inplace=True)
dataset.shape
dataset.nunique() | code |
16110432/cell_24 | [
"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
dataset = pd.read_csv('../input/Mall_Customers.csv')
dataset.rename(columns={'Annual Income (k$)': 'Annual_Income', 'Spending Score (1-100)': 'Spending_Score'}, inplace=True)
dataset.shape
dataset.nunique()
plt.figure(figsize=(15, 5))
plt.subplot(1, 2, 1)
sns.distplot(dataset['Age'])
plt.title('Age Distribution')
plt.xlabel('Age')
plt.ylabel('Count')
plt.subplot(1, 2, 2)
sns.distplot(dataset['Annual_Income'], color='pink')
plt.title('Annual Income Distribution')
plt.xlabel('Annual Income')
plt.ylabel('Count') | code |
16110432/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/Mall_Customers.csv')
dataset.rename(columns={'Annual Income (k$)': 'Annual_Income', 'Spending Score (1-100)': 'Spending_Score'}, inplace=True)
dataset.shape
dataset.describe() | code |
16110432/cell_22 | [
"text_plain_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
dataset = pd.read_csv('../input/Mall_Customers.csv')
dataset.rename(columns={'Annual Income (k$)': 'Annual_Income', 'Spending Score (1-100)': 'Spending_Score'}, inplace=True)
dataset.shape
dataset.nunique()
plt.figure(figsize=(15, 5))
sns.countplot(dataset['Annual_Income'])
plt.title('Annual Income')
plt.xlabel('Annual Income($)')
plt.show() | code |
16110432/cell_27 | [
"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
dataset = pd.read_csv('../input/Mall_Customers.csv')
dataset.rename(columns={'Annual Income (k$)': 'Annual_Income', 'Spending Score (1-100)': 'Spending_Score'}, inplace=True)
dataset.shape
dataset.nunique()
colors = ['LightBlue', 'Lightgreen']
explode = [0, 0.1]
plt.axis('off')
plt.figure(figsize=(15, 8))
sns.heatmap(dataset.corr(), annot=True) | code |
16110432/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/Mall_Customers.csv')
dataset.head() | code |
17137806/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cota = pd.read_csv('../input/cota_parlamentar_sp.csv', delimiter=';')
cota.dataframeName = 'cota_parlamentar_sp.csv'
nRow, nCol = cota.shape
#partido_valor = pd.DataFrame()
cota_negativa = pd.DataFrame()
cota_positiva = pd.DataFrame()
cota_negativa = cota[cota['vlrdocumento'] < 0]
cota_positiva = cota[cota['vlrdocumento'] > 0]
#cota_negativa['reembolso'] = pd.DataFrame()
cota_negativa['tipo'] = 'Reembolso'
cota_positiva['tipo'] = 'Gasto'
#partido_valor['sgpartido'] = cota['sgpartido']
#partido_valor['vlrdocumento'] = cota['vlrdocumento']
#partido_valor['nulegislatura'] = cota['nulegislatura']
#cota['nulegislatura'].value_counts()
#cota.groupby(['nulegislatura']).sum()
cota_total = pd.DataFrame(cota.groupby(['sgpartido'])['vlrdocumento'].sum().sort_values(ascending=False))
cota_total2 = cota_total.head(5)
cota_total3 = cota_total.tail(5)
#cota.groupby('nulegislatura')['vlrdocumento'].sum().sort_values(ascending=False)
cota_por_ano = pd.DataFrame(cota.groupby('numano')['vlrdocumento'].sum())
cota_negativa_por_ano = pd.DataFrame(cota_negativa.groupby('numano')['vlrdocumento'].sum())
#partido_valor.groupby(['sgpartido']).sum()
cota_por_ano
cota_positiva
new_cota = pd.DataFrame()
new_cota = pd.concat([cota_positiva, cota_negativa])
pd.DataFrame(new_cota['tipo'].value_counts())
labels = cota_total2.index
sizes = cota_total2['vlrdocumento']
explode = (0.1, 0.1, 0, 0, 0)
plt.axis('equal')
labels = cota_total3.index
sizes = cota_total3['vlrdocumento']
explode = (0.1, 0.1, 0, 0, 0)
plt.axis('equal')
plt.title('Evolução Gastos dos Partidos', loc='center', fontsize=12, fontweight=0, color='black')
plt.xlabel('Ano')
plt.ylabel('Gasto')
plt.plot(cota_negativa_por_ano) | code |
17137806/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cota = pd.read_csv('../input/cota_parlamentar_sp.csv', delimiter=';')
cota.dataframeName = 'cota_parlamentar_sp.csv'
nRow, nCol = cota.shape
#partido_valor = pd.DataFrame()
cota_negativa = pd.DataFrame()
cota_positiva = pd.DataFrame()
cota_negativa = cota[cota['vlrdocumento'] < 0]
cota_positiva = cota[cota['vlrdocumento'] > 0]
#cota_negativa['reembolso'] = pd.DataFrame()
cota_negativa['tipo'] = 'Reembolso'
cota_positiva['tipo'] = 'Gasto'
#partido_valor['sgpartido'] = cota['sgpartido']
#partido_valor['vlrdocumento'] = cota['vlrdocumento']
#partido_valor['nulegislatura'] = cota['nulegislatura']
#cota['nulegislatura'].value_counts()
#cota.groupby(['nulegislatura']).sum()
cota_total = pd.DataFrame(cota.groupby(['sgpartido'])['vlrdocumento'].sum().sort_values(ascending=False))
cota_total2 = cota_total.head(5)
cota_total3 = cota_total.tail(5)
#cota.groupby('nulegislatura')['vlrdocumento'].sum().sort_values(ascending=False)
cota_por_ano = pd.DataFrame(cota.groupby('numano')['vlrdocumento'].sum())
cota_negativa_por_ano = pd.DataFrame(cota_negativa.groupby('numano')['vlrdocumento'].sum())
#partido_valor.groupby(['sgpartido']).sum()
cota_por_ano
cota_positiva
new_cota = pd.DataFrame()
new_cota = pd.concat([cota_positiva, cota_negativa])
pd.DataFrame(new_cota['tipo'].value_counts())
cota_total.plot(kind='bar', title='Gastos Partidos - Completo') | code |
17137806/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cota = pd.read_csv('../input/cota_parlamentar_sp.csv', delimiter=';')
cota.dataframeName = 'cota_parlamentar_sp.csv'
nRow, nCol = cota.shape
print(f'There are {nRow} rows and {nCol} columns') | code |
17137806/cell_20 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cota = pd.read_csv('../input/cota_parlamentar_sp.csv', delimiter=';')
cota.dataframeName = 'cota_parlamentar_sp.csv'
nRow, nCol = cota.shape
#partido_valor = pd.DataFrame()
cota_negativa = pd.DataFrame()
cota_positiva = pd.DataFrame()
cota_negativa = cota[cota['vlrdocumento'] < 0]
cota_positiva = cota[cota['vlrdocumento'] > 0]
#cota_negativa['reembolso'] = pd.DataFrame()
cota_negativa['tipo'] = 'Reembolso'
cota_positiva['tipo'] = 'Gasto'
#partido_valor['sgpartido'] = cota['sgpartido']
#partido_valor['vlrdocumento'] = cota['vlrdocumento']
#partido_valor['nulegislatura'] = cota['nulegislatura']
#cota['nulegislatura'].value_counts()
#cota.groupby(['nulegislatura']).sum()
cota_total = pd.DataFrame(cota.groupby(['sgpartido'])['vlrdocumento'].sum().sort_values(ascending=False))
cota_total2 = cota_total.head(5)
cota_total3 = cota_total.tail(5)
#cota.groupby('nulegislatura')['vlrdocumento'].sum().sort_values(ascending=False)
cota_por_ano = pd.DataFrame(cota.groupby('numano')['vlrdocumento'].sum())
cota_negativa_por_ano = pd.DataFrame(cota_negativa.groupby('numano')['vlrdocumento'].sum())
#partido_valor.groupby(['sgpartido']).sum()
cota_por_ano
cota_positiva
new_cota = pd.DataFrame()
new_cota = pd.concat([cota_positiva, cota_negativa])
pd.DataFrame(new_cota['tipo'].value_counts())
labels = cota_total2.index
sizes = cota_total2['vlrdocumento']
explode = (0.1, 0.1, 0, 0, 0)
plt.axis('equal')
labels = cota_total3.index
sizes = cota_total3['vlrdocumento']
explode = (0.1, 0.1, 0, 0, 0)
plt.axis('equal')
cota_neg_ano_mes = pd.DataFrame(cota_negativa.groupby(['numano', 'nummes'])['vlrdocumento'].sum())
pd.DataFrame(cota.groupby(['numano', 'nummes'])['vlrdocumento'].sum())
val1 = pd.DataFrame(new_cota['tipo'].value_counts())
val1['perc'] = val1['tipo'] / np.cumsum(val1['tipo'], axis=0)
val1
for i, v in val1['tipo'].iteritems():
plt.text(i, v, v, va='bottom', ha='center')
plt.style.use('seaborn-darkgrid')
palette = plt.get_cmap('Set1')
num = 0
for column in cota_por_ano.drop('numano', axis=1):
num += 1
plt.plot(cota_por_ano['numano'], cota_por_ano[column], marker='', color=palette(num), linewidth=1, alpha=0.9, label=column)
plt.legend(loc=2, ncol=2)
plt.title('A (bad) Spaghetti plot', loc='left', fontsize=12, fontweight=0, color='orange')
plt.xlabel('Time')
plt.ylabel('Score') | code |
17137806/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cota = pd.read_csv('../input/cota_parlamentar_sp.csv', delimiter=';')
cota.dataframeName = 'cota_parlamentar_sp.csv'
nRow, nCol = cota.shape
#partido_valor = pd.DataFrame()
cota_negativa = pd.DataFrame()
cota_positiva = pd.DataFrame()
cota_negativa = cota[cota['vlrdocumento'] < 0]
cota_positiva = cota[cota['vlrdocumento'] > 0]
#cota_negativa['reembolso'] = pd.DataFrame()
cota_negativa['tipo'] = 'Reembolso'
cota_positiva['tipo'] = 'Gasto'
#partido_valor['sgpartido'] = cota['sgpartido']
#partido_valor['vlrdocumento'] = cota['vlrdocumento']
#partido_valor['nulegislatura'] = cota['nulegislatura']
#cota['nulegislatura'].value_counts()
#cota.groupby(['nulegislatura']).sum()
cota_total = pd.DataFrame(cota.groupby(['sgpartido'])['vlrdocumento'].sum().sort_values(ascending=False))
cota_total2 = cota_total.head(5)
cota_total3 = cota_total.tail(5)
#cota.groupby('nulegislatura')['vlrdocumento'].sum().sort_values(ascending=False)
cota_por_ano = pd.DataFrame(cota.groupby('numano')['vlrdocumento'].sum())
cota_negativa_por_ano = pd.DataFrame(cota_negativa.groupby('numano')['vlrdocumento'].sum())
#partido_valor.groupby(['sgpartido']).sum()
cota_por_ano
cota_positiva
new_cota = pd.DataFrame()
new_cota = pd.concat([cota_positiva, cota_negativa])
pd.DataFrame(new_cota['tipo'].value_counts())
labels = cota_total2.index
sizes = cota_total2['vlrdocumento']
explode = (0.1, 0.1, 0, 0, 0)
plt.axis('equal')
labels = cota_total3.index
sizes = cota_total3['vlrdocumento']
explode = (0.1, 0.1, 0, 0, 0)
plt.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%', shadow=True, startangle=140)
plt.axis('equal')
plt.show() | code |
17137806/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cota = pd.read_csv('../input/cota_parlamentar_sp.csv', delimiter=';')
cota.dataframeName = 'cota_parlamentar_sp.csv'
nRow, nCol = cota.shape
#partido_valor = pd.DataFrame()
cota_negativa = pd.DataFrame()
cota_positiva = pd.DataFrame()
cota_negativa = cota[cota['vlrdocumento'] < 0]
cota_positiva = cota[cota['vlrdocumento'] > 0]
#cota_negativa['reembolso'] = pd.DataFrame()
cota_negativa['tipo'] = 'Reembolso'
cota_positiva['tipo'] = 'Gasto'
#partido_valor['sgpartido'] = cota['sgpartido']
#partido_valor['vlrdocumento'] = cota['vlrdocumento']
#partido_valor['nulegislatura'] = cota['nulegislatura']
#cota['nulegislatura'].value_counts()
#cota.groupby(['nulegislatura']).sum()
cota_total = pd.DataFrame(cota.groupby(['sgpartido'])['vlrdocumento'].sum().sort_values(ascending=False))
cota_total2 = cota_total.head(5)
cota_total3 = cota_total.tail(5)
#cota.groupby('nulegislatura')['vlrdocumento'].sum().sort_values(ascending=False)
cota_por_ano = pd.DataFrame(cota.groupby('numano')['vlrdocumento'].sum())
cota_negativa_por_ano = pd.DataFrame(cota_negativa.groupby('numano')['vlrdocumento'].sum())
#partido_valor.groupby(['sgpartido']).sum()
cota_por_ano
cota_positiva
new_cota = pd.DataFrame()
new_cota = pd.concat([cota_positiva, cota_negativa])
pd.DataFrame(new_cota['tipo'].value_counts())
labels = cota_total2.index
sizes = cota_total2['vlrdocumento']
explode = (0.1, 0.1, 0, 0, 0)
plt.axis('equal')
labels = cota_total3.index
sizes = cota_total3['vlrdocumento']
explode = (0.1, 0.1, 0, 0, 0)
plt.axis('equal')
cota_neg_ano_mes = pd.DataFrame(cota_negativa.groupby(['numano', 'nummes'])['vlrdocumento'].sum())
pd.DataFrame(cota.groupby(['numano', 'nummes'])['vlrdocumento'].sum())
val1 = pd.DataFrame(new_cota['tipo'].value_counts())
val1['perc'] = val1['tipo'] / np.cumsum(val1['tipo'], axis=0)
val1
plt.figure(figsize=(6, 6))
for i, v in val1['tipo'].iteritems():
plt.bar(i, v, label=i)
plt.text(i, v, v, va='bottom', ha='center')
plt.title('Gastos x Reembolso')
plt.show() | code |
17137806/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cota = pd.read_csv('../input/cota_parlamentar_sp.csv', delimiter=';')
cota.dataframeName = 'cota_parlamentar_sp.csv'
nRow, nCol = cota.shape
cota_negativa = pd.DataFrame()
cota_positiva = pd.DataFrame()
cota_negativa = cota[cota['vlrdocumento'] < 0]
cota_positiva = cota[cota['vlrdocumento'] > 0]
cota_negativa['tipo'] = 'Reembolso'
cota_positiva['tipo'] = 'Gasto'
cota_total = pd.DataFrame(cota.groupby(['sgpartido'])['vlrdocumento'].sum().sort_values(ascending=False))
cota_total2 = cota_total.head(5)
cota_total3 = cota_total.tail(5)
cota_por_ano = pd.DataFrame(cota.groupby('numano')['vlrdocumento'].sum())
cota_negativa_por_ano = pd.DataFrame(cota_negativa.groupby('numano')['vlrdocumento'].sum())
cota_por_ano
cota_positiva
new_cota = pd.DataFrame()
new_cota = pd.concat([cota_positiva, cota_negativa])
pd.DataFrame(new_cota['tipo'].value_counts()) | code |
17137806/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cota = pd.read_csv('../input/cota_parlamentar_sp.csv', delimiter=';')
cota.dataframeName = 'cota_parlamentar_sp.csv'
nRow, nCol = cota.shape
#partido_valor = pd.DataFrame()
cota_negativa = pd.DataFrame()
cota_positiva = pd.DataFrame()
cota_negativa = cota[cota['vlrdocumento'] < 0]
cota_positiva = cota[cota['vlrdocumento'] > 0]
#cota_negativa['reembolso'] = pd.DataFrame()
cota_negativa['tipo'] = 'Reembolso'
cota_positiva['tipo'] = 'Gasto'
#partido_valor['sgpartido'] = cota['sgpartido']
#partido_valor['vlrdocumento'] = cota['vlrdocumento']
#partido_valor['nulegislatura'] = cota['nulegislatura']
#cota['nulegislatura'].value_counts()
#cota.groupby(['nulegislatura']).sum()
cota_total = pd.DataFrame(cota.groupby(['sgpartido'])['vlrdocumento'].sum().sort_values(ascending=False))
cota_total2 = cota_total.head(5)
cota_total3 = cota_total.tail(5)
#cota.groupby('nulegislatura')['vlrdocumento'].sum().sort_values(ascending=False)
cota_por_ano = pd.DataFrame(cota.groupby('numano')['vlrdocumento'].sum())
cota_negativa_por_ano = pd.DataFrame(cota_negativa.groupby('numano')['vlrdocumento'].sum())
#partido_valor.groupby(['sgpartido']).sum()
cota_por_ano
cota_positiva
new_cota = pd.DataFrame()
new_cota = pd.concat([cota_positiva, cota_negativa])
pd.DataFrame(new_cota['tipo'].value_counts())
cota_neg_ano_mes = pd.DataFrame(cota_negativa.groupby(['numano', 'nummes'])['vlrdocumento'].sum())
pd.DataFrame(cota.groupby(['numano', 'nummes'])['vlrdocumento'].sum())
val1 = pd.DataFrame(new_cota['tipo'].value_counts())
val1['perc'] = val1['tipo'] / np.cumsum(val1['tipo'], axis=0)
val1 | code |
17137806/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cota = pd.read_csv('../input/cota_parlamentar_sp.csv', delimiter=';')
cota.dataframeName = 'cota_parlamentar_sp.csv'
nRow, nCol = cota.shape
#partido_valor = pd.DataFrame()
cota_negativa = pd.DataFrame()
cota_positiva = pd.DataFrame()
cota_negativa = cota[cota['vlrdocumento'] < 0]
cota_positiva = cota[cota['vlrdocumento'] > 0]
#cota_negativa['reembolso'] = pd.DataFrame()
cota_negativa['tipo'] = 'Reembolso'
cota_positiva['tipo'] = 'Gasto'
#partido_valor['sgpartido'] = cota['sgpartido']
#partido_valor['vlrdocumento'] = cota['vlrdocumento']
#partido_valor['nulegislatura'] = cota['nulegislatura']
#cota['nulegislatura'].value_counts()
#cota.groupby(['nulegislatura']).sum()
cota_total = pd.DataFrame(cota.groupby(['sgpartido'])['vlrdocumento'].sum().sort_values(ascending=False))
cota_total2 = cota_total.head(5)
cota_total3 = cota_total.tail(5)
#cota.groupby('nulegislatura')['vlrdocumento'].sum().sort_values(ascending=False)
cota_por_ano = pd.DataFrame(cota.groupby('numano')['vlrdocumento'].sum())
cota_negativa_por_ano = pd.DataFrame(cota_negativa.groupby('numano')['vlrdocumento'].sum())
#partido_valor.groupby(['sgpartido']).sum()
cota_por_ano
cota_positiva
new_cota = pd.DataFrame()
new_cota = pd.concat([cota_positiva, cota_negativa])
pd.DataFrame(new_cota['tipo'].value_counts())
cota_total | code |
17137806/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cota = pd.read_csv('../input/cota_parlamentar_sp.csv', delimiter=';')
cota.dataframeName = 'cota_parlamentar_sp.csv'
nRow, nCol = cota.shape
#partido_valor = pd.DataFrame()
cota_negativa = pd.DataFrame()
cota_positiva = pd.DataFrame()
cota_negativa = cota[cota['vlrdocumento'] < 0]
cota_positiva = cota[cota['vlrdocumento'] > 0]
#cota_negativa['reembolso'] = pd.DataFrame()
cota_negativa['tipo'] = 'Reembolso'
cota_positiva['tipo'] = 'Gasto'
#partido_valor['sgpartido'] = cota['sgpartido']
#partido_valor['vlrdocumento'] = cota['vlrdocumento']
#partido_valor['nulegislatura'] = cota['nulegislatura']
#cota['nulegislatura'].value_counts()
#cota.groupby(['nulegislatura']).sum()
cota_total = pd.DataFrame(cota.groupby(['sgpartido'])['vlrdocumento'].sum().sort_values(ascending=False))
cota_total2 = cota_total.head(5)
cota_total3 = cota_total.tail(5)
#cota.groupby('nulegislatura')['vlrdocumento'].sum().sort_values(ascending=False)
cota_por_ano = pd.DataFrame(cota.groupby('numano')['vlrdocumento'].sum())
cota_negativa_por_ano = pd.DataFrame(cota_negativa.groupby('numano')['vlrdocumento'].sum())
#partido_valor.groupby(['sgpartido']).sum()
cota_por_ano
cota_positiva
new_cota = pd.DataFrame()
new_cota = pd.concat([cota_positiva, cota_negativa])
pd.DataFrame(new_cota['tipo'].value_counts())
cota.describe() | code |
17137806/cell_16 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cota = pd.read_csv('../input/cota_parlamentar_sp.csv', delimiter=';')
cota.dataframeName = 'cota_parlamentar_sp.csv'
nRow, nCol = cota.shape
#partido_valor = pd.DataFrame()
cota_negativa = pd.DataFrame()
cota_positiva = pd.DataFrame()
cota_negativa = cota[cota['vlrdocumento'] < 0]
cota_positiva = cota[cota['vlrdocumento'] > 0]
#cota_negativa['reembolso'] = pd.DataFrame()
cota_negativa['tipo'] = 'Reembolso'
cota_positiva['tipo'] = 'Gasto'
#partido_valor['sgpartido'] = cota['sgpartido']
#partido_valor['vlrdocumento'] = cota['vlrdocumento']
#partido_valor['nulegislatura'] = cota['nulegislatura']
#cota['nulegislatura'].value_counts()
#cota.groupby(['nulegislatura']).sum()
cota_total = pd.DataFrame(cota.groupby(['sgpartido'])['vlrdocumento'].sum().sort_values(ascending=False))
cota_total2 = cota_total.head(5)
cota_total3 = cota_total.tail(5)
#cota.groupby('nulegislatura')['vlrdocumento'].sum().sort_values(ascending=False)
cota_por_ano = pd.DataFrame(cota.groupby('numano')['vlrdocumento'].sum())
cota_negativa_por_ano = pd.DataFrame(cota_negativa.groupby('numano')['vlrdocumento'].sum())
#partido_valor.groupby(['sgpartido']).sum()
cota_por_ano
cota_positiva
new_cota = pd.DataFrame()
new_cota = pd.concat([cota_positiva, cota_negativa])
pd.DataFrame(new_cota['tipo'].value_counts())
cota_neg_ano_mes = pd.DataFrame(cota_negativa.groupby(['numano', 'nummes'])['vlrdocumento'].sum())
pd.DataFrame(cota.groupby(['numano', 'nummes'])['vlrdocumento'].sum()) | code |
17137806/cell_17 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cota = pd.read_csv('../input/cota_parlamentar_sp.csv', delimiter=';')
cota.dataframeName = 'cota_parlamentar_sp.csv'
nRow, nCol = cota.shape
#partido_valor = pd.DataFrame()
cota_negativa = pd.DataFrame()
cota_positiva = pd.DataFrame()
cota_negativa = cota[cota['vlrdocumento'] < 0]
cota_positiva = cota[cota['vlrdocumento'] > 0]
#cota_negativa['reembolso'] = pd.DataFrame()
cota_negativa['tipo'] = 'Reembolso'
cota_positiva['tipo'] = 'Gasto'
#partido_valor['sgpartido'] = cota['sgpartido']
#partido_valor['vlrdocumento'] = cota['vlrdocumento']
#partido_valor['nulegislatura'] = cota['nulegislatura']
#cota['nulegislatura'].value_counts()
#cota.groupby(['nulegislatura']).sum()
cota_total = pd.DataFrame(cota.groupby(['sgpartido'])['vlrdocumento'].sum().sort_values(ascending=False))
cota_total2 = cota_total.head(5)
cota_total3 = cota_total.tail(5)
#cota.groupby('nulegislatura')['vlrdocumento'].sum().sort_values(ascending=False)
cota_por_ano = pd.DataFrame(cota.groupby('numano')['vlrdocumento'].sum())
cota_negativa_por_ano = pd.DataFrame(cota_negativa.groupby('numano')['vlrdocumento'].sum())
#partido_valor.groupby(['sgpartido']).sum()
cota_por_ano
cota_positiva
new_cota = pd.DataFrame()
new_cota = pd.concat([cota_positiva, cota_negativa])
pd.DataFrame(new_cota['tipo'].value_counts())
cota_neg_ano_mes = pd.DataFrame(cota_negativa.groupby(['numano', 'nummes'])['vlrdocumento'].sum())
cota_negativa.describe() | code |
17137806/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cota = pd.read_csv('../input/cota_parlamentar_sp.csv', delimiter=';')
cota.dataframeName = 'cota_parlamentar_sp.csv'
nRow, nCol = cota.shape
#partido_valor = pd.DataFrame()
cota_negativa = pd.DataFrame()
cota_positiva = pd.DataFrame()
cota_negativa = cota[cota['vlrdocumento'] < 0]
cota_positiva = cota[cota['vlrdocumento'] > 0]
#cota_negativa['reembolso'] = pd.DataFrame()
cota_negativa['tipo'] = 'Reembolso'
cota_positiva['tipo'] = 'Gasto'
#partido_valor['sgpartido'] = cota['sgpartido']
#partido_valor['vlrdocumento'] = cota['vlrdocumento']
#partido_valor['nulegislatura'] = cota['nulegislatura']
#cota['nulegislatura'].value_counts()
#cota.groupby(['nulegislatura']).sum()
cota_total = pd.DataFrame(cota.groupby(['sgpartido'])['vlrdocumento'].sum().sort_values(ascending=False))
cota_total2 = cota_total.head(5)
cota_total3 = cota_total.tail(5)
#cota.groupby('nulegislatura')['vlrdocumento'].sum().sort_values(ascending=False)
cota_por_ano = pd.DataFrame(cota.groupby('numano')['vlrdocumento'].sum())
cota_negativa_por_ano = pd.DataFrame(cota_negativa.groupby('numano')['vlrdocumento'].sum())
#partido_valor.groupby(['sgpartido']).sum()
cota_por_ano
cota_positiva
new_cota = pd.DataFrame()
new_cota = pd.concat([cota_positiva, cota_negativa])
pd.DataFrame(new_cota['tipo'].value_counts())
labels = cota_total2.index
sizes = cota_total2['vlrdocumento']
explode = (0.1, 0.1, 0, 0, 0)
plt.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%', shadow=True, startangle=140)
plt.axis('equal')
plt.show() | code |
17137806/cell_12 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cota = pd.read_csv('../input/cota_parlamentar_sp.csv', delimiter=';')
cota.dataframeName = 'cota_parlamentar_sp.csv'
nRow, nCol = cota.shape
#partido_valor = pd.DataFrame()
cota_negativa = pd.DataFrame()
cota_positiva = pd.DataFrame()
cota_negativa = cota[cota['vlrdocumento'] < 0]
cota_positiva = cota[cota['vlrdocumento'] > 0]
#cota_negativa['reembolso'] = pd.DataFrame()
cota_negativa['tipo'] = 'Reembolso'
cota_positiva['tipo'] = 'Gasto'
#partido_valor['sgpartido'] = cota['sgpartido']
#partido_valor['vlrdocumento'] = cota['vlrdocumento']
#partido_valor['nulegislatura'] = cota['nulegislatura']
#cota['nulegislatura'].value_counts()
#cota.groupby(['nulegislatura']).sum()
cota_total = pd.DataFrame(cota.groupby(['sgpartido'])['vlrdocumento'].sum().sort_values(ascending=False))
cota_total2 = cota_total.head(5)
cota_total3 = cota_total.tail(5)
#cota.groupby('nulegislatura')['vlrdocumento'].sum().sort_values(ascending=False)
cota_por_ano = pd.DataFrame(cota.groupby('numano')['vlrdocumento'].sum())
cota_negativa_por_ano = pd.DataFrame(cota_negativa.groupby('numano')['vlrdocumento'].sum())
#partido_valor.groupby(['sgpartido']).sum()
cota_por_ano
cota_positiva
new_cota = pd.DataFrame()
new_cota = pd.concat([cota_positiva, cota_negativa])
pd.DataFrame(new_cota['tipo'].value_counts())
labels = cota_total2.index
sizes = cota_total2['vlrdocumento']
explode = (0.1, 0.1, 0, 0, 0)
plt.axis('equal')
labels = cota_total3.index
sizes = cota_total3['vlrdocumento']
explode = (0.1, 0.1, 0, 0, 0)
plt.axis('equal')
plt.title('Evolução Gastos dos Partidos', loc='center', fontsize=12, fontweight=0, color='black')
plt.xlabel('Ano')
plt.ylabel('Gasto')
plt.plot(cota_por_ano) | code |
17137806/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cota = pd.read_csv('../input/cota_parlamentar_sp.csv', delimiter=';')
cota.dataframeName = 'cota_parlamentar_sp.csv'
nRow, nCol = cota.shape
cota.head(10) | code |
73064668/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
test_df = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
cat_features = [feature for feature in train_df.columns if 'cat' in feature]
cont_features = [feature for feature in train_df.columns if 'cont' in feature]
train_df.describe().T
train_df.drop('id', axis=1, inplace=True)
test_df.drop('id', axis=1, inplace=True)
print('Missing values in train dataset:', sum(train_df.isnull().mean() * 100))
print('Missing values in test dataset:', sum(test_df.isnull().mean() * 100)) | code |
73064668/cell_11 | [
"text_plain_output_1.png"
] | from pandas_profiling import ProfileReport
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
test_df = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
cat_features = [feature for feature in train_df.columns if 'cat' in feature]
cont_features = [feature for feature in train_df.columns if 'cont' in feature]
train_df.describe().T
train_df.drop('id', axis=1, inplace=True)
test_df.drop('id', axis=1, inplace=True)
report = ProfileReport(train_df)
report | code |
73064668/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 |
73064668/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
test_df = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
cat_features = [feature for feature in train_df.columns if 'cat' in feature]
cont_features = [feature for feature in train_df.columns if 'cont' in feature]
print('Rows and Columns in train dataset:', train_df.shape)
print('Rows and Columns in test dataset:', test_df.shape) | code |
73064668/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
test_df = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
cat_features = [feature for feature in train_df.columns if 'cat' in feature]
cont_features = [feature for feature in train_df.columns if 'cont' in feature]
train_df.describe().T | code |
73064668/cell_15 | [
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.preprocessing import OrdinalEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
test_df = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
cat_features = [feature for feature in train_df.columns if 'cat' in feature]
cont_features = [feature for feature in train_df.columns if 'cont' in feature]
train_df.describe().T
train_df.drop('id', axis=1, inplace=True)
test_df.drop('id', axis=1, inplace=True)
x = train_df.drop(['target'], axis=1)
y = train_df['target']
X_test = test_df.copy()
from sklearn.preprocessing import OrdinalEncoder
ordinal_encoder = OrdinalEncoder()
x[cat_features] = ordinal_encoder.fit_transform(x[cat_features])
X_test[cat_features] = ordinal_encoder.transform(X_test[cat_features])
x.head() | code |
105195066/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
df_test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
sample = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
df_train.isnull().sum()
df_train.nunique()
print('Products in Train Data: ', df_train['product'].unique())
print()
print('Value Counts of Products:', df_train['product'].value_counts()) | code |
105195066/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
df_test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
sample = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
print('Countries in Test Dataset: ', df_test['country'].unique())
print('NULL VALUES : ', df_test['country'].isnull().sum()) | code |
105195066/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
df_test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
sample = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
df_train.isnull().sum() | code |
105195066/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
df_test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
sample = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
df_train.isnull().sum()
df_train.nunique()
df_train['num_sold'] | code |
105195066/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
df_test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
sample = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
print('Products in test Data: ', df_test['product'].unique())
print()
print('Value Counts of Products:', df_test['product'].value_counts()) | code |
105195066/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
df_test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
sample = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
sample | code |
105195066/cell_26 | [
"text_plain_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
df_train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
df_test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
sample = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
df_train.isnull().sum()
df_train.nunique()
plt.title('No of products Sold in Each Country')
sns.barplot(data=df_train, y=df_train['num_sold'], x=df_train['country']) | code |
105195066/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
df_test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
sample = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
df_train.isnull().sum()
df_train.nunique()
df_train.info() | code |
105195066/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 |
105195066/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
df_test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
sample = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
df_train.info() | code |
105195066/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
df_test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
sample = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
df_train | code |
105195066/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
df_test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
sample = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
df_train.isnull().sum()
df_train.nunique()
print('Stores in Train Data: ', df_train['store'].unique())
print()
print('Value Counts of Stores:', df_train['store'].value_counts()) | code |
105195066/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
df_test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
sample = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
print('Stores in Train Data: ', df_test['store'].unique())
print()
print('Value Counts of Stores:', df_test['store'].value_counts()) | code |
105195066/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
df_test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
sample = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
df_train.isnull().sum()
df_train.nunique()
print('Num solf in Train Data: ', df_train['num_sold'].nunique())
print()
print('Value Counts of Sold by :', df_train['num_sold'].value_counts()) | code |
105195066/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
df_test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
sample = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
df_train.isnull().sum()
df_train.nunique() | code |
105195066/cell_27 | [
"text_plain_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
df_train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
df_test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
sample = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
df_train.isnull().sum()
df_train.nunique()
plt.title('No of products Sold in Each Store In Each Country')
sns.barplot(data=df_train, y=df_train['num_sold'], x=df_train['country'], hue='store') | code |
105195066/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
df_test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
sample = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
df_train.isnull().sum()
df_train.nunique()
print('Countries in Train Dataset: ', df_train['country'].unique())
print('NULL VALUES : ', df_train['country'].isnull().sum()) | code |
48163903/cell_13 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import re
df_train = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
df_test = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
df_sub = pd.read_csv('/kaggle/input/nlp-getting-started/sample_submission.csv')
def preprocessing(df):
derlem = []
for i in range(len(df.text)):
text = re.sub('https?://\\S+', '', df.text[i])
text = re.sub('http?://\\S+', '', text)
text = re.sub('[^a-zA-Z]', ' ', text)
text = re.sub('\\n', ' ', text)
text = re.sub('\\s+', ' ', text).strip()
text = text.lower()
text = text.split()
text = [WordNetLemmatizer().lemmatize(kelime) for kelime in text if not kelime in set(stopwords.words('english'))]
text = ' '.join(text)
derlem.append(text)
df['clean_text'] = derlem
return df
df_test = preprocessing(df_test)
df_train = preprocessing(df_train)
tfidf = TfidfVectorizer(min_df=0.0, max_df=1.0, use_idf=True)
X = tfidf.fit_transform(df_train.text).toarray()
y = df_train.target
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
lr = LogisticRegression(solver='liblinear')
lgr_classifier = Pipeline([('scale', scaler), ('lr', lr)])
lgr_classifier.fit(X_train, y_train)
lgr_prediction = lgr_classifier.predict(X_test)
accuracy_score(y_train, lgr_classifier.predict(X_train))
test_vector = tfidf.transform(df_test['clean_text']).toarray()
prediction = lgr_classifier.predict(test_vector)
prediction | code |
48163903/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import MinMaxScaler
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
lr = LogisticRegression(solver='liblinear')
lgr_classifier = Pipeline([('scale', scaler), ('lr', lr)])
lgr_classifier.fit(X_train, y_train)
lgr_prediction = lgr_classifier.predict(X_test)
accuracy_score(y_test, lgr_prediction) | code |
48163903/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
df_test = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
df_sub = pd.read_csv('/kaggle/input/nlp-getting-started/sample_submission.csv')
df_train.head() | code |
48163903/cell_14 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import re
df_train = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
df_test = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
df_sub = pd.read_csv('/kaggle/input/nlp-getting-started/sample_submission.csv')
def preprocessing(df):
derlem = []
for i in range(len(df.text)):
text = re.sub('https?://\\S+', '', df.text[i])
text = re.sub('http?://\\S+', '', text)
text = re.sub('[^a-zA-Z]', ' ', text)
text = re.sub('\\n', ' ', text)
text = re.sub('\\s+', ' ', text).strip()
text = text.lower()
text = text.split()
text = [WordNetLemmatizer().lemmatize(kelime) for kelime in text if not kelime in set(stopwords.words('english'))]
text = ' '.join(text)
derlem.append(text)
df['clean_text'] = derlem
return df
df_test = preprocessing(df_test)
df_train = preprocessing(df_train)
tfidf = TfidfVectorizer(min_df=0.0, max_df=1.0, use_idf=True)
X = tfidf.fit_transform(df_train.text).toarray()
y = df_train.target
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
lr = LogisticRegression(solver='liblinear')
lgr_classifier = Pipeline([('scale', scaler), ('lr', lr)])
lgr_classifier.fit(X_train, y_train)
lgr_prediction = lgr_classifier.predict(X_test)
accuracy_score(y_train, lgr_classifier.predict(X_train))
test_vector = tfidf.transform(df_test['clean_text']).toarray()
prediction = lgr_classifier.predict(test_vector)
prediction
df_submission = pd.DataFrame()
df_submission['id'] = df_test['id']
df_submission['target'] = prediction
df_submission.head(10) | code |
48163903/cell_10 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import MinMaxScaler
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
lr = LogisticRegression(solver='liblinear')
lgr_classifier = Pipeline([('scale', scaler), ('lr', lr)])
lgr_classifier.fit(X_train, y_train)
lgr_prediction = lgr_classifier.predict(X_test)
accuracy_score(y_train, lgr_classifier.predict(X_train)) | code |
48163903/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import MinMaxScaler
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
lr = LogisticRegression(solver='liblinear')
lgr_classifier = Pipeline([('scale', scaler), ('lr', lr)])
lgr_classifier.fit(X_train, y_train)
lgr_prediction = lgr_classifier.predict(X_test)
print(classification_report(y_test, lgr_prediction))
print(confusion_matrix(y_test, lgr_prediction)) | code |
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