path
stringlengths 13
17
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sequencelengths 1
873
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stringlengths 0
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stringclasses 1
value |
|---|---|---|---|
18147692/cell_45 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
data_age1.reset_index(inplace=True)
datacount = dataset.groupby('age').count()
datacount = datacount.reset_index()
data1000 = datacount[datacount['tenure'] >= 1000]
dataset_1k = data_age1.loc[data_age1['age'].isin(data1000['age'])]
dataset_1k[dataset_1k['tenure'] == dataset_1k['tenure'].max()]
data2000 = datacount[datacount['tenure'] >= 2000]
dataset_2k = data_age1.loc[data_age1['age'].isin(data2000['age'])]
dataset_2k[dataset_2k['tenure'] == dataset_2k['tenure'].max()]
dataset_2k['mobile_surfing'] = dataset_2k.mobile_likes + dataset_2k.mobile_likes_received
dataset_2k['web_surfing'] = dataset_2k.www_likes + dataset_2k.www_likes_received
dataset_2k[dataset_2k['mobile_likes'] == dataset_2k['mobile_likes'].max()] | code |
18147692/cell_32 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
datacount = dataset.groupby('age').count()
datacount = datacount.reset_index()
data1000 = datacount[datacount['tenure'] >= 1000]
dataset_gender_male = dataset[dataset['gender'] == 'male']
dataset_gender_female = dataset[dataset['gender'] == 'female']
dataset_gender_male.shape | code |
18147692/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
data_age1.head() | code |
18147692/cell_15 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
data_age1.reset_index(inplace=True)
plt.bar(data_age1.age, data_age1.tenure)
plt.xlabel('Age')
plt.ylabel('Tenure') | code |
18147692/cell_38 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
data_age1.reset_index(inplace=True)
datacount = dataset.groupby('age').count()
datacount = datacount.reset_index()
data1000 = datacount[datacount['tenure'] >= 1000]
dataset_1k = data_age1.loc[data_age1['age'].isin(data1000['age'])]
dataset_1k[dataset_1k['tenure'] == dataset_1k['tenure'].max()]
data2000 = datacount[datacount['tenure'] >= 2000]
dataset_2k = data_age1.loc[data_age1['age'].isin(data2000['age'])]
dataset_2k[dataset_2k['tenure'] == dataset_2k['tenure'].max()]
data_age1['mobile_surfing'] = data_age1.mobile_likes + data_age1.mobile_likes_received
data_age1['web_surfing'] = data_age1.www_likes + data_age1.www_likes_received
data_age1[data_age1.mobile_surfing > data_age1.web_surfing].shape | code |
18147692/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.head() | code |
18147692/cell_17 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
data_age1.reset_index(inplace=True)
data_age1[data_age1['tenure'] == data_age1['tenure'].max()] | code |
18147692/cell_35 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
data_age1.reset_index(inplace=True)
datacount = dataset.groupby('age').count()
datacount = datacount.reset_index()
data1000 = datacount[datacount['tenure'] >= 1000]
dataset_1k = data_age1.loc[data_age1['age'].isin(data1000['age'])]
dataset_1k[dataset_1k['tenure'] == dataset_1k['tenure'].max()]
data2000 = datacount[datacount['tenure'] >= 2000]
dataset_2k = data_age1.loc[data_age1['age'].isin(data2000['age'])]
dataset_2k[dataset_2k['tenure'] == dataset_2k['tenure'].max()]
data_age1['mobile_surfing'] = data_age1.mobile_likes + data_age1.mobile_likes_received
data_age1['web_surfing'] = data_age1.www_likes + data_age1.www_likes_received
data_age1.head() | code |
18147692/cell_43 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
data_age1.reset_index(inplace=True)
datacount = dataset.groupby('age').count()
datacount = datacount.reset_index()
data1000 = datacount[datacount['tenure'] >= 1000]
dataset_1k = data_age1.loc[data_age1['age'].isin(data1000['age'])]
dataset_1k[dataset_1k['tenure'] == dataset_1k['tenure'].max()]
data2000 = datacount[datacount['tenure'] >= 2000]
dataset_2k = data_age1.loc[data_age1['age'].isin(data2000['age'])]
dataset_2k[dataset_2k['tenure'] == dataset_2k['tenure'].max()]
dataset_2k['mobile_surfing'] = dataset_2k.mobile_likes + dataset_2k.mobile_likes_received
dataset_2k['web_surfing'] = dataset_2k.www_likes + dataset_2k.www_likes_received
dataset_2k[dataset_2k['mobile_surfing'] == dataset_2k['mobile_surfing'].max()] | code |
18147692/cell_24 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
data_age1.reset_index(inplace=True)
datacount = dataset.groupby('age').count()
datacount = datacount.reset_index()
data1000 = datacount[datacount['tenure'] >= 1000]
dataset_1k = data_age1.loc[data_age1['age'].isin(data1000['age'])]
dataset_1k[dataset_1k['tenure'] == dataset_1k['tenure'].max()]
data2000 = datacount[datacount['tenure'] >= 2000]
dataset_2k = data_age1.loc[data_age1['age'].isin(data2000['age'])]
dataset_2k[dataset_2k['tenure'] == dataset_2k['tenure'].max()]
data_age1[data_age1['friend_count'] == data_age1['friend_count'].max()] | code |
18147692/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
data_age1.reset_index(inplace=True)
datacount = dataset.groupby('age').count()
datacount = datacount.reset_index()
data1000 = datacount[datacount['tenure'] >= 1000]
dataset_1k = data_age1.loc[data_age1['age'].isin(data1000['age'])]
dataset_1k[dataset_1k['tenure'] == dataset_1k['tenure'].max()]
data2000 = datacount[datacount['tenure'] >= 2000]
dataset_2k = data_age1.loc[data_age1['age'].isin(data2000['age'])]
dataset_2k[dataset_2k['tenure'] == dataset_2k['tenure'].max()] | code |
18147692/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
data_age1.reset_index(inplace=True)
data_age1.head() | code |
18147692/cell_27 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
data_age1.reset_index(inplace=True)
datacount = dataset.groupby('age').count()
datacount = datacount.reset_index()
data1000 = datacount[datacount['tenure'] >= 1000]
dataset_1k = data_age1.loc[data_age1['age'].isin(data1000['age'])]
dataset_1k[dataset_1k['tenure'] == dataset_1k['tenure'].max()]
dataset_1k[dataset_1k['friend_count'] == dataset_1k['friend_count'].max()] | code |
18147692/cell_36 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
data_age1.reset_index(inplace=True)
datacount = dataset.groupby('age').count()
datacount = datacount.reset_index()
data1000 = datacount[datacount['tenure'] >= 1000]
dataset_1k = data_age1.loc[data_age1['age'].isin(data1000['age'])]
dataset_1k[dataset_1k['tenure'] == dataset_1k['tenure'].max()]
data2000 = datacount[datacount['tenure'] >= 2000]
dataset_2k = data_age1.loc[data_age1['age'].isin(data2000['age'])]
dataset_2k[dataset_2k['tenure'] == dataset_2k['tenure'].max()]
data_age1['mobile_surfing'] = data_age1.mobile_likes + data_age1.mobile_likes_received
data_age1['web_surfing'] = data_age1.www_likes + data_age1.www_likes_received
plt.figure(figsize=(10, 7))
plt.plot('age', 'mobile_surfing', 'bv--', data=data_age1)
plt.plot('age', 'web_surfing', 'r*-', data=data_age1)
plt.xlabel('Age')
plt.ylabel('Mobile and Web Surfing')
plt.title('Age Vs Surfing')
plt.legend() | code |
105203552/cell_21 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"image_output_5.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"image_output_4.png",
"image_output_6.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_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/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_df.columns
train_df[['Sex', 'Survived']].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False) | code |
105203552/cell_9 | [
"image_output_2.png",
"image_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/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_df.columns
train_df.info() | code |
105203552/cell_23 | [
"image_output_3.png",
"image_output_2.png",
"image_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/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_df.columns
train_df[['Parch', 'Survived']].groupby(['Parch'], as_index=False).mean().sort_values(by='Survived', ascending=False) | code |
105203552/cell_30 | [
"text_html_output_1.png"
] | from collections import Counter
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_df.columns
def detect_outliers(df, features):
outlier_indices = []
for c in features:
Q1 = np.percentile(df[c], 25)
Q3 = np.percentile(df[c], 75)
IQR = Q3 - Q1
outlier_step = IQR * 1.5
outlier_list_col = df[(df[c] < Q1 - outlier_step) | (df[c] > Q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((i for i, v in outlier_indices.items() if v > 2))
return multiple_outliers
train_df.loc[detect_outliers(train_df, ['Age', 'SibSp', 'Parch', 'Fare'])]
tarin_df = train_df.drop(detect_outliers(train_df, ['Age', 'SibSp', 'Parch', 'Fare']), axis=0).reset_index(drop=True)
train_df_len = len(train_df)
train_df = pd.concat([train_df, test_df], axis=0).reset_index(drop=True)
train_df.head() | code |
105203552/cell_33 | [
"text_html_output_1.png"
] | from collections import Counter
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_df.columns
def detect_outliers(df, features):
outlier_indices = []
for c in features:
Q1 = np.percentile(df[c], 25)
Q3 = np.percentile(df[c], 75)
IQR = Q3 - Q1
outlier_step = IQR * 1.5
outlier_list_col = df[(df[c] < Q1 - outlier_step) | (df[c] > Q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((i for i, v in outlier_indices.items() if v > 2))
return multiple_outliers
train_df.loc[detect_outliers(train_df, ['Age', 'SibSp', 'Parch', 'Fare'])]
tarin_df = train_df.drop(detect_outliers(train_df, ['Age', 'SibSp', 'Parch', 'Fare']), axis=0).reset_index(drop=True)
train_df_len = len(train_df)
train_df = pd.concat([train_df, test_df], axis=0).reset_index(drop=True)
train_df.columns[train_df.isnull().any()]
train_df.isnull().sum() | code |
105203552/cell_44 | [
"text_html_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
import seaborn as sns
from collections import Counter
import warnings
warnings.filterwarnings('ignore')
import os
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_df.columns
def bar_plot(variable):
"""
input: variable eg: "Sex"
output: bar plot & value count
"""
var = train_df[variable]
varValue = var.value_counts()
plt.xticks(varValue.index, varValue.index.values)
def plot_hist(variable):
pass
def detect_outliers(df, features):
outlier_indices = []
for c in features:
Q1 = np.percentile(df[c], 25)
Q3 = np.percentile(df[c], 75)
IQR = Q3 - Q1
outlier_step = IQR * 1.5
outlier_list_col = df[(df[c] < Q1 - outlier_step) | (df[c] > Q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((i for i, v in outlier_indices.items() if v > 2))
return multiple_outliers
train_df.loc[detect_outliers(train_df, ['Age', 'SibSp', 'Parch', 'Fare'])]
tarin_df = train_df.drop(detect_outliers(train_df, ['Age', 'SibSp', 'Parch', 'Fare']), axis=0).reset_index(drop=True)
train_df_len = len(train_df)
train_df = pd.concat([train_df, test_df], axis=0).reset_index(drop=True)
train_df.columns[train_df.isnull().any()]
train_df.isnull().sum()
sns.factorplot(x='Sex', y='Age', data=train_df, kind='box')
plt.show() | code |
105203552/cell_20 | [
"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/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_df.columns
train_df[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean().sort_values(by='Survived', ascending=False) | code |
105203552/cell_6 | [
"image_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/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_df.columns
train_df.tail() | code |
105203552/cell_39 | [
"text_html_output_1.png"
] | from collections import Counter
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_df.columns
def detect_outliers(df, features):
outlier_indices = []
for c in features:
Q1 = np.percentile(df[c], 25)
Q3 = np.percentile(df[c], 75)
IQR = Q3 - Q1
outlier_step = IQR * 1.5
outlier_list_col = df[(df[c] < Q1 - outlier_step) | (df[c] > Q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((i for i, v in outlier_indices.items() if v > 2))
return multiple_outliers
train_df.loc[detect_outliers(train_df, ['Age', 'SibSp', 'Parch', 'Fare'])]
tarin_df = train_df.drop(detect_outliers(train_df, ['Age', 'SibSp', 'Parch', 'Fare']), axis=0).reset_index(drop=True)
train_df_len = len(train_df)
train_df = pd.concat([train_df, test_df], axis=0).reset_index(drop=True)
train_df.columns[train_df.isnull().any()]
train_df.isnull().sum()
train_df[train_df['Pclass'] == 3] | code |
105203552/cell_26 | [
"text_html_output_1.png"
] | from collections import Counter
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_df.columns
def detect_outliers(df, features):
outlier_indices = []
for c in features:
Q1 = np.percentile(df[c], 25)
Q3 = np.percentile(df[c], 75)
IQR = Q3 - Q1
outlier_step = IQR * 1.5
outlier_list_col = df[(df[c] < Q1 - outlier_step) | (df[c] > Q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((i for i, v in outlier_indices.items() if v > 2))
return multiple_outliers
train_df.loc[detect_outliers(train_df, ['Age', 'SibSp', 'Parch', 'Fare'])] | code |
105203552/cell_41 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from collections import Counter
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_df.columns
def detect_outliers(df, features):
outlier_indices = []
for c in features:
Q1 = np.percentile(df[c], 25)
Q3 = np.percentile(df[c], 75)
IQR = Q3 - Q1
outlier_step = IQR * 1.5
outlier_list_col = df[(df[c] < Q1 - outlier_step) | (df[c] > Q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((i for i, v in outlier_indices.items() if v > 2))
return multiple_outliers
train_df.loc[detect_outliers(train_df, ['Age', 'SibSp', 'Parch', 'Fare'])]
tarin_df = train_df.drop(detect_outliers(train_df, ['Age', 'SibSp', 'Parch', 'Fare']), axis=0).reset_index(drop=True)
train_df_len = len(train_df)
train_df = pd.concat([train_df, test_df], axis=0).reset_index(drop=True)
train_df.columns[train_df.isnull().any()]
train_df.isnull().sum()
train_df[train_df['Fare'].isnull()] | code |
105203552/cell_2 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import os
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
import seaborn as sns
from collections import Counter
import warnings
warnings.filterwarnings('ignore')
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
105203552/cell_7 | [
"image_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/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_df.columns
train_df.describe() | code |
105203552/cell_45 | [
"text_html_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
import seaborn as sns
from collections import Counter
import warnings
warnings.filterwarnings('ignore')
import os
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_df.columns
def bar_plot(variable):
"""
input: variable eg: "Sex"
output: bar plot & value count
"""
var = train_df[variable]
varValue = var.value_counts()
plt.xticks(varValue.index, varValue.index.values)
def plot_hist(variable):
pass
def detect_outliers(df, features):
outlier_indices = []
for c in features:
Q1 = np.percentile(df[c], 25)
Q3 = np.percentile(df[c], 75)
IQR = Q3 - Q1
outlier_step = IQR * 1.5
outlier_list_col = df[(df[c] < Q1 - outlier_step) | (df[c] > Q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((i for i, v in outlier_indices.items() if v > 2))
return multiple_outliers
train_df.loc[detect_outliers(train_df, ['Age', 'SibSp', 'Parch', 'Fare'])]
tarin_df = train_df.drop(detect_outliers(train_df, ['Age', 'SibSp', 'Parch', 'Fare']), axis=0).reset_index(drop=True)
train_df_len = len(train_df)
train_df = pd.concat([train_df, test_df], axis=0).reset_index(drop=True)
train_df.columns[train_df.isnull().any()]
train_df.isnull().sum()
sns.factorplot(x='Sex', y='Age', hue='Pclass', data=train_df, kind='box')
plt.show() | code |
105203552/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
import seaborn as sns
from collections import Counter
import warnings
warnings.filterwarnings('ignore')
import os
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_df.columns
def bar_plot(variable):
"""
input: variable eg: "Sex"
output: bar plot & value count
"""
var = train_df[variable]
varValue = var.value_counts()
plt.xticks(varValue.index, varValue.index.values)
def plot_hist(variable):
pass
numericVar = ['Fare', 'Age', 'PassengerId']
for n in numericVar:
plot_hist(n) | code |
105203552/cell_32 | [
"text_html_output_1.png"
] | from collections import Counter
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_df.columns
def detect_outliers(df, features):
outlier_indices = []
for c in features:
Q1 = np.percentile(df[c], 25)
Q3 = np.percentile(df[c], 75)
IQR = Q3 - Q1
outlier_step = IQR * 1.5
outlier_list_col = df[(df[c] < Q1 - outlier_step) | (df[c] > Q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((i for i, v in outlier_indices.items() if v > 2))
return multiple_outliers
train_df.loc[detect_outliers(train_df, ['Age', 'SibSp', 'Parch', 'Fare'])]
tarin_df = train_df.drop(detect_outliers(train_df, ['Age', 'SibSp', 'Parch', 'Fare']), axis=0).reset_index(drop=True)
train_df_len = len(train_df)
train_df = pd.concat([train_df, test_df], axis=0).reset_index(drop=True)
train_df.columns[train_df.isnull().any()] | code |
105203552/cell_15 | [
"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/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_df.columns
category2 = ['Cabin', 'Name', 'Ticket']
for c in category2:
print('{} \n'.format(train_df[c].value_counts())) | code |
105203552/cell_38 | [
"text_plain_output_1.png"
] | from collections import Counter
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_df.columns
def detect_outliers(df, features):
outlier_indices = []
for c in features:
Q1 = np.percentile(df[c], 25)
Q3 = np.percentile(df[c], 75)
IQR = Q3 - Q1
outlier_step = IQR * 1.5
outlier_list_col = df[(df[c] < Q1 - outlier_step) | (df[c] > Q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((i for i, v in outlier_indices.items() if v > 2))
return multiple_outliers
train_df.loc[detect_outliers(train_df, ['Age', 'SibSp', 'Parch', 'Fare'])]
tarin_df = train_df.drop(detect_outliers(train_df, ['Age', 'SibSp', 'Parch', 'Fare']), axis=0).reset_index(drop=True)
train_df_len = len(train_df)
train_df = pd.concat([train_df, test_df], axis=0).reset_index(drop=True)
train_df.columns[train_df.isnull().any()]
train_df.isnull().sum()
train_df[train_df['Fare'].isnull()] | code |
105203552/cell_35 | [
"text_html_output_1.png"
] | from collections import Counter
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_df.columns
def detect_outliers(df, features):
outlier_indices = []
for c in features:
Q1 = np.percentile(df[c], 25)
Q3 = np.percentile(df[c], 75)
IQR = Q3 - Q1
outlier_step = IQR * 1.5
outlier_list_col = df[(df[c] < Q1 - outlier_step) | (df[c] > Q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((i for i, v in outlier_indices.items() if v > 2))
return multiple_outliers
train_df.loc[detect_outliers(train_df, ['Age', 'SibSp', 'Parch', 'Fare'])]
tarin_df = train_df.drop(detect_outliers(train_df, ['Age', 'SibSp', 'Parch', 'Fare']), axis=0).reset_index(drop=True)
train_df_len = len(train_df)
train_df = pd.concat([train_df, test_df], axis=0).reset_index(drop=True)
train_df.columns[train_df.isnull().any()]
train_df.isnull().sum()
train_df[train_df['Embarked'].isnull()] | code |
105203552/cell_43 | [
"text_html_output_1.png"
] | from collections import Counter
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_df.columns
def detect_outliers(df, features):
outlier_indices = []
for c in features:
Q1 = np.percentile(df[c], 25)
Q3 = np.percentile(df[c], 75)
IQR = Q3 - Q1
outlier_step = IQR * 1.5
outlier_list_col = df[(df[c] < Q1 - outlier_step) | (df[c] > Q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((i for i, v in outlier_indices.items() if v > 2))
return multiple_outliers
train_df.loc[detect_outliers(train_df, ['Age', 'SibSp', 'Parch', 'Fare'])]
tarin_df = train_df.drop(detect_outliers(train_df, ['Age', 'SibSp', 'Parch', 'Fare']), axis=0).reset_index(drop=True)
train_df_len = len(train_df)
train_df = pd.concat([train_df, test_df], axis=0).reset_index(drop=True)
train_df.columns[train_df.isnull().any()]
train_df.isnull().sum()
train_df[train_df['Age'].isnull()] | code |
105203552/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
import seaborn as sns
from collections import Counter
import warnings
warnings.filterwarnings('ignore')
import os
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_df.columns
def bar_plot(variable):
"""
input: variable eg: "Sex"
output: bar plot & value count
"""
var = train_df[variable]
varValue = var.value_counts()
plt.xticks(varValue.index, varValue.index.values)
category1 = ['Survived', 'Sex', 'Pclass', 'Embarked', 'SibSp', 'Parch']
for c in category1:
bar_plot(c) | code |
105203552/cell_22 | [
"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/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_df.columns
train_df[['SibSp', 'Survived']].groupby(['SibSp'], as_index=False).mean().sort_values(by='Survived', ascending=False) | code |
105203552/cell_37 | [
"text_plain_output_1.png"
] | from collections import Counter
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_df.columns
def detect_outliers(df, features):
outlier_indices = []
for c in features:
Q1 = np.percentile(df[c], 25)
Q3 = np.percentile(df[c], 75)
IQR = Q3 - Q1
outlier_step = IQR * 1.5
outlier_list_col = df[(df[c] < Q1 - outlier_step) | (df[c] > Q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((i for i, v in outlier_indices.items() if v > 2))
return multiple_outliers
train_df.loc[detect_outliers(train_df, ['Age', 'SibSp', 'Parch', 'Fare'])]
tarin_df = train_df.drop(detect_outliers(train_df, ['Age', 'SibSp', 'Parch', 'Fare']), axis=0).reset_index(drop=True)
train_df_len = len(train_df)
train_df = pd.concat([train_df, test_df], axis=0).reset_index(drop=True)
train_df.columns[train_df.isnull().any()]
train_df.isnull().sum()
train_df['Embarked'] = train_df['Embarked'].fillna('C')
train_df[train_df['Embarked'].isnull()] | code |
105203552/cell_5 | [
"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/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_df.columns | code |
105203552/cell_36 | [
"text_html_output_1.png"
] | from collections import Counter
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_df.columns
def detect_outliers(df, features):
outlier_indices = []
for c in features:
Q1 = np.percentile(df[c], 25)
Q3 = np.percentile(df[c], 75)
IQR = Q3 - Q1
outlier_step = IQR * 1.5
outlier_list_col = df[(df[c] < Q1 - outlier_step) | (df[c] > Q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((i for i, v in outlier_indices.items() if v > 2))
return multiple_outliers
train_df.loc[detect_outliers(train_df, ['Age', 'SibSp', 'Parch', 'Fare'])]
tarin_df = train_df.drop(detect_outliers(train_df, ['Age', 'SibSp', 'Parch', 'Fare']), axis=0).reset_index(drop=True)
train_df_len = len(train_df)
train_df = pd.concat([train_df, test_df], axis=0).reset_index(drop=True)
train_df.columns[train_df.isnull().any()]
train_df.isnull().sum()
train_df.boxplot(column='Fare', by='Embarked') | code |
106199562/cell_15 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
headers = ['symboling', 'normalized-losses', 'make', 'fuel-type', 'aspiration', 'num-of-doors', 'body-style', 'drive-wheels', 'engine-location', 'wheel-base', 'length', 'width', 'height', 'curb-weight', 'engine-type', 'num-of-cylinders', 'engine-size', 'fuel-system', 'bore', 'stroke', 'compression-ratio', 'horsepower', 'peak-rpm', 'city-mpg', 'highway-mpg', 'price']
df = pd.read_csv('../input/ucmachinelearning/imports-85.data', names=headers)
df.replace('?', np.nan, inplace=True)
df.head(5) | code |
106199562/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
headers = ['symboling', 'normalized-losses', 'make', 'fuel-type', 'aspiration', 'num-of-doors', 'body-style', 'drive-wheels', 'engine-location', 'wheel-base', 'length', 'width', 'height', 'curb-weight', 'engine-type', 'num-of-cylinders', 'engine-size', 'fuel-system', 'bore', 'stroke', 'compression-ratio', 'horsepower', 'peak-rpm', 'city-mpg', 'highway-mpg', 'price']
df = pd.read_csv('../input/ucmachinelearning/imports-85.data', names=headers)
df.head(10) | code |
16153941/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sn
filename = '../input/bike_sharing_hourly.csv'
data = pd.read_csv(filename)
data.isnull().sum()
data.rename(columns={'weathersit': 'weather', 'mnth': 'month', 'hr': 'hour', 'yr': 'year', 'hum': 'humidity', 'cnt': 'count'}, inplace=True)
data.dtypes
data = data.drop(['instant', 'dteday'], axis=1)
data['year'] = data.year.astype('category')
data['season'] = data.season.astype('category')
data['month'] = data.month.astype('category')
data['hour'] = data.hour.astype('category')
data['holiday'] = data.holiday.astype('category')
data['weekday'] = data.weekday.astype('category')
data['workingday'] = data.workingday.astype('category')
data['weather'] = data.weather.astype('category')
data.dtypes
## Exploratory Data Analysis
# Analyzing the change in bike sharing pattern('count' variable in dataset) with categorical variables
fig,[ax1,ax2,ax3,ax4,ax5,ax6,ax7,ax8] = plt.subplots(nrows=8, figsize=(15,25))
sn.barplot(x = data['weekday'], y = data['count'],ax = ax1)
sn.barplot(x = data['season'], y = data['count'],ax = ax2)
sn.barplot(x = data['month'], y = data['count'],ax = ax3)
sn.barplot(x = data['holiday'], y = data['count'],ax = ax4)
sn.barplot(x = data['hour'], y = data['count'],ax = ax5)
sn.barplot(x = data['weather'], y = data['count'],ax = ax6)
sn.barplot(x = data['workingday'], y = data['count'],ax = ax7)
sn.barplot(x = data['year'], y = data['count'],ax = ax8)
# Total bike users(count) is sum of registered and casual users. Need to analyze how they vary individually with hour
# The variation is observed in different circumstances to check how those impact the bike users
fig,axes = plt.subplots(nrows = 3,ncols = 3, figsize=(25,30))
sn.pointplot(x = 'hour', y = 'registered', hue = 'month',data = data,ax = axes[0][0])
sn.pointplot(x = 'hour', y = 'casual', hue = 'month', data = data,ax = axes[0][1])
sn.pointplot(x = 'hour', y = 'count', hue = 'month', size = 7, data = data,ax = axes[0][2])
sn.pointplot(x = 'hour', y = 'registered', hue = 'season',data = data,ax = axes[1][0])
sn.pointplot(x = 'hour', y = 'casual', hue = 'season', data = data,ax = axes[1][1])
sn.pointplot(x = 'hour', y = 'count', hue = 'season', size = 7, data = data,ax = axes[1][2])
sn.pointplot(x = 'hour', y = 'registered', hue = 'weather',data = data,ax = axes[2][0])
sn.pointplot(x = 'hour', y = 'casual', hue = 'weather', data = data,ax = axes[2][1])
sn.pointplot(x = 'hour', y = 'count', hue = 'weather', size = 7, data = data,ax = axes[2][2])
fig, [ax1, ax2, ax3] = plt.subplots(ncols=3, figsize=(20, 8))
plt.rc('xtick', labelsize=10)
plt.rc('ytick', labelsize=10)
sn.regplot(x='temp', y='count', data=data, ax=ax1)
ax1.set(title='Relation between temperature and count')
sn.regplot(x='humidity', y='count', data=data, ax=ax2)
ax2.set(title='Relation between humidity and total count')
sn.regplot(x='windspeed', y='count', data=data, ax=ax3)
ax3.set(title='Relation between Windspeed and total count') | code |
16153941/cell_9 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sn
filename = '../input/bike_sharing_hourly.csv'
data = pd.read_csv(filename)
data.isnull().sum()
data.rename(columns={'weathersit': 'weather', 'mnth': 'month', 'hr': 'hour', 'yr': 'year', 'hum': 'humidity', 'cnt': 'count'}, inplace=True)
data.dtypes
data = data.drop(['instant', 'dteday'], axis=1)
data['year'] = data.year.astype('category')
data['season'] = data.season.astype('category')
data['month'] = data.month.astype('category')
data['hour'] = data.hour.astype('category')
data['holiday'] = data.holiday.astype('category')
data['weekday'] = data.weekday.astype('category')
data['workingday'] = data.workingday.astype('category')
data['weather'] = data.weather.astype('category')
data.dtypes
fig, [ax1, ax2, ax3, ax4, ax5, ax6, ax7, ax8] = plt.subplots(nrows=8, figsize=(15, 25))
sn.barplot(x=data['weekday'], y=data['count'], ax=ax1)
sn.barplot(x=data['season'], y=data['count'], ax=ax2)
sn.barplot(x=data['month'], y=data['count'], ax=ax3)
sn.barplot(x=data['holiday'], y=data['count'], ax=ax4)
sn.barplot(x=data['hour'], y=data['count'], ax=ax5)
sn.barplot(x=data['weather'], y=data['count'], ax=ax6)
sn.barplot(x=data['workingday'], y=data['count'], ax=ax7)
sn.barplot(x=data['year'], y=data['count'], ax=ax8) | code |
16153941/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
filename = '../input/bike_sharing_hourly.csv'
data = pd.read_csv(filename)
data.isnull().sum() | code |
16153941/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
filename = '../input/bike_sharing_hourly.csv'
data = pd.read_csv(filename)
data.isnull().sum()
data.rename(columns={'weathersit': 'weather', 'mnth': 'month', 'hr': 'hour', 'yr': 'year', 'hum': 'humidity', 'cnt': 'count'}, inplace=True)
data.head(2) | code |
16153941/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sn
filename = '../input/bike_sharing_hourly.csv'
data = pd.read_csv(filename)
data.isnull().sum()
data.rename(columns={'weathersit': 'weather', 'mnth': 'month', 'hr': 'hour', 'yr': 'year', 'hum': 'humidity', 'cnt': 'count'}, inplace=True)
data.dtypes
data = data.drop(['instant', 'dteday'], axis=1)
data['year'] = data.year.astype('category')
data['season'] = data.season.astype('category')
data['month'] = data.month.astype('category')
data['hour'] = data.hour.astype('category')
data['holiday'] = data.holiday.astype('category')
data['weekday'] = data.weekday.astype('category')
data['workingday'] = data.workingday.astype('category')
data['weather'] = data.weather.astype('category')
data.dtypes
## Exploratory Data Analysis
# Analyzing the change in bike sharing pattern('count' variable in dataset) with categorical variables
fig,[ax1,ax2,ax3,ax4,ax5,ax6,ax7,ax8] = plt.subplots(nrows=8, figsize=(15,25))
sn.barplot(x = data['weekday'], y = data['count'],ax = ax1)
sn.barplot(x = data['season'], y = data['count'],ax = ax2)
sn.barplot(x = data['month'], y = data['count'],ax = ax3)
sn.barplot(x = data['holiday'], y = data['count'],ax = ax4)
sn.barplot(x = data['hour'], y = data['count'],ax = ax5)
sn.barplot(x = data['weather'], y = data['count'],ax = ax6)
sn.barplot(x = data['workingday'], y = data['count'],ax = ax7)
sn.barplot(x = data['year'], y = data['count'],ax = ax8)
fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(25, 30))
sn.pointplot(x='hour', y='registered', hue='month', data=data, ax=axes[0][0])
sn.pointplot(x='hour', y='casual', hue='month', data=data, ax=axes[0][1])
sn.pointplot(x='hour', y='count', hue='month', size=7, data=data, ax=axes[0][2])
sn.pointplot(x='hour', y='registered', hue='season', data=data, ax=axes[1][0])
sn.pointplot(x='hour', y='casual', hue='season', data=data, ax=axes[1][1])
sn.pointplot(x='hour', y='count', hue='season', size=7, data=data, ax=axes[1][2])
sn.pointplot(x='hour', y='registered', hue='weather', data=data, ax=axes[2][0])
sn.pointplot(x='hour', y='casual', hue='weather', data=data, ax=axes[2][1])
sn.pointplot(x='hour', y='count', hue='weather', size=7, data=data, ax=axes[2][2]) | code |
16153941/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
filename = '../input/bike_sharing_hourly.csv'
data = pd.read_csv(filename)
data.isnull().sum()
data.rename(columns={'weathersit': 'weather', 'mnth': 'month', 'hr': 'hour', 'yr': 'year', 'hum': 'humidity', 'cnt': 'count'}, inplace=True)
data.dtypes | code |
16153941/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
filename = '../input/bike_sharing_hourly.csv'
data = pd.read_csv(filename)
data.isnull().sum()
data.rename(columns={'weathersit': 'weather', 'mnth': 'month', 'hr': 'hour', 'yr': 'year', 'hum': 'humidity', 'cnt': 'count'}, inplace=True)
data.dtypes
data = data.drop(['instant', 'dteday'], axis=1)
data['year'] = data.year.astype('category')
data['season'] = data.season.astype('category')
data['month'] = data.month.astype('category')
data['hour'] = data.hour.astype('category')
data['holiday'] = data.holiday.astype('category')
data['weekday'] = data.weekday.astype('category')
data['workingday'] = data.workingday.astype('category')
data['weather'] = data.weather.astype('category')
data.dtypes | code |
16153941/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
filename = '../input/bike_sharing_hourly.csv'
data = pd.read_csv(filename)
data.head(2) | code |
32067376/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
avocado = pd.read_csv('../input/avocados/Avocado.csv')
avocado
avocado.index
avocado.columns
avocado.shape
avo_data = avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.mean()
avocado.groupby(['year']).Small_Bags.median()
avocado.groupby(['year']).Small_Bags.std()
avocado.groupby(['year']).Small_Bags.size()
avocado.groupby(['year']).Small_Bags.value_counts()
new_avo = avocado.groupby('year')['Small_Bags', 'Large_Bags'].sum()
new_avo
new_avo.loc[2015] | code |
32067376/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)
avocado = pd.read_csv('../input/avocados/Avocado.csv')
avocado
avocado.index
avocado.columns
avocado.shape | code |
32067376/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
avocado = pd.read_csv('../input/avocados/Avocado.csv')
avocado
avocado.index
avocado.columns
avocado.shape
avo_data = avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.mean()
avocado.groupby(['year']).Small_Bags.median()
avocado.groupby(['year']).Small_Bags.std()
avocado.groupby(['year']).Small_Bags.size()
avocado.groupby(['year']).Small_Bags.value_counts()
new_avo = avocado.groupby('year')['Small_Bags', 'Large_Bags'].sum()
new_avo
avo_sorted_small = avocado.sort_values('Small_Bags', ascending=True)
avo_sorted_small
avo_sorted_small.sort_index() | code |
32067376/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
avocado = pd.read_csv('../input/avocados/Avocado.csv')
avocado
avocado.head() | code |
32067376/cell_26 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
avocado = pd.read_csv('../input/avocados/Avocado.csv')
avocado
avocado.index
avocado.columns
avocado.shape
avo_data = avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.mean()
avocado.groupby(['year']).Small_Bags.median()
avocado.groupby(['year']).Small_Bags.std()
avocado.groupby(['year']).Small_Bags.size()
avocado.groupby(['year']).Small_Bags.value_counts()
new_avo = avocado.groupby('year')['Small_Bags', 'Large_Bags'].sum()
new_avo
avo_sorted_small = avocado.sort_values('Small_Bags', ascending=True)
avo_sorted_small
avo_sorted_small.sort_index()
avo_sorted_small.max() | code |
32067376/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
avocado = pd.read_csv('../input/avocados/Avocado.csv')
avocado | code |
32067376/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
avocado = pd.read_csv('../input/avocados/Avocado.csv')
avocado
avocado.index
avocado.columns
avocado.shape
avo_data = avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.sum() | code |
32067376/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
avocado = pd.read_csv('../input/avocados/Avocado.csv')
avocado
avocado.index
avocado.columns
avocado.shape
avo_data = avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.mean()
avocado.groupby(['year']).Small_Bags.median()
avocado.groupby(['year']).Small_Bags.std()
avocado.groupby(['year']).Small_Bags.size()
avocado.groupby(['year']).Small_Bags.value_counts()
new_avo = avocado.groupby('year')['Small_Bags', 'Large_Bags'].sum()
new_avo | code |
32067376/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 |
32067376/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
avocado = pd.read_csv('../input/avocados/Avocado.csv')
avocado
avocado.index | code |
32067376/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
avocado = pd.read_csv('../input/avocados/Avocado.csv')
avocado
avocado.index
avocado.columns
avocado.shape
avo_data = avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.mean()
avocado.groupby(['year']).Small_Bags.median()
avocado.groupby(['year']).Small_Bags.std()
avocado.groupby(['year']).Small_Bags.size()
avocado.groupby(['year']).Small_Bags.value_counts() | code |
32067376/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
avocado = pd.read_csv('../input/avocados/Avocado.csv')
avocado
avocado.index
avocado.columns
avocado.shape
avo_data = avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.mean()
avocado.groupby(['year']).Small_Bags.median()
avocado.groupby(['year']).Small_Bags.std()
avocado.groupby(['year']).Small_Bags.size()
avocado.groupby(['year']).Small_Bags.value_counts()
new_avo = avocado.groupby('year')['Small_Bags', 'Large_Bags'].sum()
new_avo
avo_sorted_small = avocado.sort_values('Small_Bags', ascending=True)
avo_sorted_small
avocado.groupby('year')['Small_Bags'].sum().plot.bar() | code |
32067376/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
avocado = pd.read_csv('../input/avocados/Avocado.csv')
avocado
avocado.index
avocado.columns | code |
32067376/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
avocado = pd.read_csv('../input/avocados/Avocado.csv')
avocado
avocado.index
avocado.columns
avocado.shape
avo_data = avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.mean()
avocado.groupby(['year']).Small_Bags.median() | code |
32067376/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
avocado = pd.read_csv('../input/avocados/Avocado.csv')
avocado
avocado.index
avocado.columns
avocado.shape
avo_data = avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.mean()
avocado.groupby(['year']).Small_Bags.median()
avocado.groupby(['year']).Small_Bags.std() | code |
32067376/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
avocado = pd.read_csv('../input/avocados/Avocado.csv')
avocado
avocado.index
avocado.columns
avocado.shape
avo_data = avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.mean()
avocado.groupby(['year']).Small_Bags.median()
avocado.groupby(['year']).Small_Bags.std()
avocado.groupby(['year']).Small_Bags.size() | code |
32067376/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
avocado = pd.read_csv('../input/avocados/Avocado.csv')
avocado
avocado.index
avocado.columns
avocado.shape
avo_data = avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.mean()
avocado.groupby(['year']).Small_Bags.median()
avocado.groupby(['year']).Small_Bags.std()
avocado.groupby(['year']).Small_Bags.size()
avocado.groupby(['year']).Small_Bags.value_counts()
new_avo = avocado.groupby('year')['Small_Bags', 'Large_Bags'].sum()
new_avo
avo_sorted_small = avocado.sort_values('Small_Bags', ascending=True)
avo_sorted_small | code |
32067376/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
avocado = pd.read_csv('../input/avocados/Avocado.csv')
avocado
avocado.index
avocado.columns
avocado.shape
avo_data = avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.mean() | code |
32067376/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
avocado = pd.read_csv('../input/avocados/Avocado.csv')
avocado
avocado.index
avocado.columns
avocado.shape
avo_data = avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.sum()
avocado.groupby(['year']).Small_Bags.mean()
avocado.groupby(['year']).Small_Bags.median()
avocado.groupby(['year']).Small_Bags.std()
avocado.groupby(['year']).Small_Bags.size()
avocado.groupby(['year']).Small_Bags.value_counts()
new_avo = avocado.groupby('year')['Small_Bags', 'Large_Bags'].sum()
new_avo
new_avo.loc[2015]
new_avo.iloc[1, 1] | code |
32067376/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
avocado = pd.read_csv('../input/avocados/Avocado.csv')
avocado
avocado.tail(3) | code |
72104176/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_vals = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_values.csv')
train_labels = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_labels.csv')
train_vals.dtypes.value_counts()
train_df = train_vals.merge(train_labels, on='building_id')
corr = train_df.corr()['damage_grade'].sort_values(ascending=False)[1:]
corr = corr[abs(corr.values) > 0.01]
cats = []
for column in train_df.columns:
if train_df[column].dtype == 'object':
cats.append(column)
for cat in cats:
train_df[cat] = train_df[cat].astype('category')
len(cats)
from sklearn.preprocessing import LabelEncoder
lb_make = LabelEncoder()
for cat in cats:
train_df[cat] = lb_make.fit_transform(train_df[cat])
test_vals = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/test_values.csv')
for cat in cats:
test_vals[cat] = lb_make.fit_transform(test_vals[cat])
test_vals['Predicted_damage_grade'].value_counts() | code |
72104176/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_vals = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_values.csv')
train_labels = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_labels.csv')
train_vals.dtypes.value_counts()
train_df = train_vals.merge(train_labels, on='building_id')
numericals = ['count_floors_pre_eq', 'height_percentage', 'area_percentage', 'count_families']
fig, axes = plt.subplots(2,2, figsize = (10,10))
for i, feature in enumerate(numericals):
sns.histplot(data = train_df, x = feature, ax = axes[i%2, i//2], color = 'purple')
plt.show()
corr = train_df.corr()['damage_grade'].sort_values(ascending=False)[1:]
corr = corr[abs(corr.values) > 0.01]
plt.xticks(rotation=90)
cats = []
for column in train_df.columns:
if train_df[column].dtype == 'object':
cats.append(column)
for cat in cats:
train_df[cat] = train_df[cat].astype('category')
len(cats)
sns.set_palette('colorblind')
fig, axes = plt.subplots(4, 2, figsize=(20, 20), sharex=False)
for i, name in enumerate(cats):
sns.countplot(data=train_df, x=name, ax=axes[i % 4, i // 4])
plt.show() | code |
72104176/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_vals = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_values.csv')
train_labels = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_labels.csv')
train_vals.dtypes.value_counts()
train_df = train_vals.merge(train_labels, on='building_id')
numericals = ['count_floors_pre_eq', 'height_percentage', 'area_percentage', 'count_families']
fig, axes = plt.subplots(2, 2, figsize=(10, 10))
for i, feature in enumerate(numericals):
sns.histplot(data=train_df, x=feature, ax=axes[i % 2, i // 2], color='purple')
plt.show() | code |
72104176/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_vals = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_values.csv')
train_labels = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_labels.csv')
print(train_vals.head())
print(train_labels.head()) | code |
72104176/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_vals = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_values.csv')
train_labels = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_labels.csv')
train_vals.info() | code |
72104176/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_vals = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_values.csv')
train_labels = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_labels.csv')
train_vals.dtypes.value_counts()
train_df = train_vals.merge(train_labels, on='building_id')
numericals = ['count_floors_pre_eq', 'height_percentage', 'area_percentage', 'count_families']
fig, axes = plt.subplots(2,2, figsize = (10,10))
for i, feature in enumerate(numericals):
sns.histplot(data = train_df, x = feature, ax = axes[i%2, i//2], color = 'purple')
plt.show()
corr = train_df.corr()['damage_grade'].sort_values(ascending=False)[1:]
corr = corr[abs(corr.values) > 0.01]
plt.figure(figsize=(10, 10))
sns.barplot(x=corr.index, y=corr, color='teal')
plt.xticks(rotation=90)
plt.show() | code |
72104176/cell_19 | [
"image_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_vals = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_values.csv')
train_labels = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_labels.csv')
train_vals.dtypes.value_counts()
train_df = train_vals.merge(train_labels, on='building_id')
corr = train_df.corr()['damage_grade'].sort_values(ascending=False)[1:]
corr = corr[abs(corr.values) > 0.01]
cats = []
for column in train_df.columns:
if train_df[column].dtype == 'object':
cats.append(column)
for cat in cats:
train_df[cat] = train_df[cat].astype('category')
len(cats)
from sklearn.preprocessing import LabelEncoder
lb_make = LabelEncoder()
for cat in cats:
train_df[cat] = lb_make.fit_transform(train_df[cat])
test_vals = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/test_values.csv')
for cat in cats:
test_vals[cat] = lb_make.fit_transform(test_vals[cat])
test_vals.head() | code |
72104176/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 |
72104176/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_vals = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_values.csv')
train_labels = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_labels.csv')
train_vals.dtypes.value_counts() | code |
72104176/cell_18 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_vals = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_values.csv')
train_labels = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_labels.csv')
test_vals = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/test_values.csv')
test_vals.head() | code |
72104176/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_vals = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_values.csv')
train_labels = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_labels.csv')
train_vals.dtypes.value_counts()
train_df = train_vals.merge(train_labels, on='building_id')
train_df.head() | code |
72104176/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_vals = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_values.csv')
train_labels = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_labels.csv')
train_vals.dtypes.value_counts()
train_df = train_vals.merge(train_labels, on='building_id')
corr = train_df.corr()['damage_grade'].sort_values(ascending=False)[1:]
corr = corr[abs(corr.values) > 0.01]
cats = []
for column in train_df.columns:
if train_df[column].dtype == 'object':
cats.append(column)
for cat in cats:
train_df[cat] = train_df[cat].astype('category')
len(cats)
from sklearn.preprocessing import LabelEncoder
lb_make = LabelEncoder()
for cat in cats:
train_df[cat] = lb_make.fit_transform(train_df[cat])
train_df.head() | code |
72104176/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_vals = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_values.csv')
train_labels = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_labels.csv')
train_vals.dtypes.value_counts()
train_df = train_vals.merge(train_labels, on='building_id')
corr = train_df.corr()['damage_grade'].sort_values(ascending=False)[1:]
corr = corr[abs(corr.values) > 0.01]
cats = []
for column in train_df.columns:
if train_df[column].dtype == 'object':
cats.append(column)
for cat in cats:
train_df[cat] = train_df[cat].astype('category')
len(cats)
from sklearn.preprocessing import LabelEncoder
lb_make = LabelEncoder()
for cat in cats:
train_df[cat] = lb_make.fit_transform(train_df[cat])
train_df['damage_grade'].value_counts() | code |
72104176/cell_3 | [
"image_output_1.png"
] | import matplotlib
import matplotlib
for cname in matplotlib.colors.cnames:
print(cname) | code |
72104176/cell_17 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_vals = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_values.csv')
train_labels = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_labels.csv')
train_vals.dtypes.value_counts()
train_df = train_vals.merge(train_labels, on='building_id')
corr = train_df.corr()['damage_grade'].sort_values(ascending=False)[1:]
corr = corr[abs(corr.values) > 0.01]
cats = []
for column in train_df.columns:
if train_df[column].dtype == 'object':
cats.append(column)
for cat in cats:
train_df[cat] = train_df[cat].astype('category')
len(cats)
from sklearn.preprocessing import LabelEncoder
lb_make = LabelEncoder()
for cat in cats:
train_df[cat] = lb_make.fit_transform(train_df[cat])
logreg = LogisticRegression()
X = train_df.drop('damage_grade', axis=1)
y = train_df['damage_grade']
logreg.fit(X, y) | code |
72104176/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_vals = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_values.csv')
train_labels = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_labels.csv')
train_vals.dtypes.value_counts()
train_df = train_vals.merge(train_labels, on='building_id')
corr = train_df.corr()['damage_grade'].sort_values(ascending=False)[1:]
corr = corr[abs(corr.values) > 0.01]
cats = []
for column in train_df.columns:
if train_df[column].dtype == 'object':
cats.append(column)
for cat in cats:
train_df[cat] = train_df[cat].astype('category')
len(cats) | code |
72104176/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_vals = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_values.csv')
train_labels = pd.read_csv('../input/richters-predictor-modeling-earthquake-damage/train_labels.csv')
train_vals.head() | code |
128034496/cell_4 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('/kaggle/input/business-decision-research/data_retail.csv', sep=';')
print('Lima data teratas:')
print(df.head())
print('\nInfo dataset:')
print(df.info()) | code |
128034496/cell_6 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('/kaggle/input/business-decision-research/data_retail.csv', sep=';')
df['First_Transaction'] = pd.to_datetime(df['First_Transaction'] / 1000, unit='s', origin='1970-01-01')
df['Last_Transaction'] = pd.to_datetime(df['Last_Transaction'] / 1000, unit='s', origin='1970-01-01')
print('Lima data teratas:')
print(df.head())
print('\nInfo dataset:')
print(df.info()) | code |
128034496/cell_18 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('/kaggle/input/business-decision-research/data_retail.csv', sep=';')
df['First_Transaction'] = pd.to_datetime(df['First_Transaction'] / 1000, unit='s', origin='1970-01-01')
df['Last_Transaction'] = pd.to_datetime(df['Last_Transaction'] / 1000, unit='s', origin='1970-01-01')
df.loc[df['Last_Transaction'] <= '2018-08-01', 'is_churn'] = True
df.loc[df['Last_Transaction'] > '2018-08-01', 'is_churn'] = False
del df['no']
del df['Row_Num']
df['Year_First_Transaction'] = df['First_Transaction'].dt.year
df['Year_Last_Transaction'] = df['Last_Transaction'].dt.year
df_year = df.groupby(['Year_First_Transaction'])['Customer_ID'].count()
plt.tight_layout()
plt.clf()
df_year = df.groupby(['Year_First_Transaction'])['Count_Transaction'].sum()
plt.tight_layout()
plt.tight_layout()
df_piv = df.pivot_table(index='is_churn', columns='Product', values='Customer_ID', aggfunc='count', fill_value=0)
plot_product = df_piv.count().sort_values(ascending=False).head(5).index
df_piv = df_piv.reindex(columns=plot_product)
df_piv.plot.pie(subplots=True, figsize=(10, 7), layout=(-1, 2), autopct='%1.0f%%', title='Proportion Churn by Product')
plt.tight_layout()
plt.show() | code |
128034496/cell_8 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('/kaggle/input/business-decision-research/data_retail.csv', sep=';')
df['First_Transaction'] = pd.to_datetime(df['First_Transaction'] / 1000, unit='s', origin='1970-01-01')
df['Last_Transaction'] = pd.to_datetime(df['Last_Transaction'] / 1000, unit='s', origin='1970-01-01')
print(max(df['Last_Transaction']))
df.loc[df['Last_Transaction'] <= '2018-08-01', 'is_churn'] = True
df.loc[df['Last_Transaction'] > '2018-08-01', 'is_churn'] = False
print('Lima data teratas:')
print(df.head())
print('\nInfo dataset:')
print(df.info()) | code |
128034496/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('/kaggle/input/business-decision-research/data_retail.csv', sep=';')
df['First_Transaction'] = pd.to_datetime(df['First_Transaction'] / 1000, unit='s', origin='1970-01-01')
df['Last_Transaction'] = pd.to_datetime(df['Last_Transaction'] / 1000, unit='s', origin='1970-01-01')
df.loc[df['Last_Transaction'] <= '2018-08-01', 'is_churn'] = True
df.loc[df['Last_Transaction'] > '2018-08-01', 'is_churn'] = False
del df['no']
del df['Row_Num']
df['Year_First_Transaction'] = df['First_Transaction'].dt.year
df['Year_Last_Transaction'] = df['Last_Transaction'].dt.year
df_year = df.groupby(['Year_First_Transaction'])['Customer_ID'].count()
plt.tight_layout()
plt.clf()
df_year = df.groupby(['Year_First_Transaction'])['Count_Transaction'].sum()
plt.tight_layout()
sns.pointplot(data=df.groupby(['Product', 'Year_First_Transaction']).mean(numeric_only=True).reset_index(), x='Year_First_Transaction', y='Average_Transaction_Amount', hue='Product')
plt.tight_layout()
plt.show() | code |
128034496/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('/kaggle/input/business-decision-research/data_retail.csv', sep=';')
df['First_Transaction'] = pd.to_datetime(df['First_Transaction'] / 1000, unit='s', origin='1970-01-01')
df['Last_Transaction'] = pd.to_datetime(df['Last_Transaction'] / 1000, unit='s', origin='1970-01-01')
df.loc[df['Last_Transaction'] <= '2018-08-01', 'is_churn'] = True
df.loc[df['Last_Transaction'] > '2018-08-01', 'is_churn'] = False
del df['no']
del df['Row_Num']
df['Year_First_Transaction'] = df['First_Transaction'].dt.year
df['Year_Last_Transaction'] = df['Last_Transaction'].dt.year
df_year = df.groupby(['Year_First_Transaction'])['Customer_ID'].count()
plt.tight_layout()
plt.clf()
df_year = df.groupby(['Year_First_Transaction'])['Count_Transaction'].sum()
df_year.plot(x='Year_First_Transaction', y='Count_Transaction', kind='bar', title='Graph of Transaction Customer')
plt.xlabel('Year_First_Transaction')
plt.ylabel('Num_of_Transaction')
plt.tight_layout()
plt.show() | code |
128034496/cell_10 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('/kaggle/input/business-decision-research/data_retail.csv', sep=';')
df['First_Transaction'] = pd.to_datetime(df['First_Transaction'] / 1000, unit='s', origin='1970-01-01')
df['Last_Transaction'] = pd.to_datetime(df['Last_Transaction'] / 1000, unit='s', origin='1970-01-01')
df.loc[df['Last_Transaction'] <= '2018-08-01', 'is_churn'] = True
df.loc[df['Last_Transaction'] > '2018-08-01', 'is_churn'] = False
del df['no']
del df['Row_Num']
print(df.head()) | code |
128034496/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('/kaggle/input/business-decision-research/data_retail.csv', sep=';')
df['First_Transaction'] = pd.to_datetime(df['First_Transaction'] / 1000, unit='s', origin='1970-01-01')
df['Last_Transaction'] = pd.to_datetime(df['Last_Transaction'] / 1000, unit='s', origin='1970-01-01')
df.loc[df['Last_Transaction'] <= '2018-08-01', 'is_churn'] = True
df.loc[df['Last_Transaction'] > '2018-08-01', 'is_churn'] = False
del df['no']
del df['Row_Num']
df['Year_First_Transaction'] = df['First_Transaction'].dt.year
df['Year_Last_Transaction'] = df['Last_Transaction'].dt.year
df_year = df.groupby(['Year_First_Transaction'])['Customer_ID'].count()
df_year.plot(x='Year_First_Transaction', y='Customer_ID', kind='bar', title='Graph of Customer Acquisition')
plt.xlabel('Year_First_Transaction')
plt.ylabel('Num_of_Customer')
plt.tight_layout()
plt.show() | code |
130012258/cell_25 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('/kaggle/input/midjourney-v5-prompts-and-links/MJ_Part3.csv')
df
df = df.drop(['AuthorID', 'Author', 'Date', 'Reactions', 'Attachments'], axis=1)
df
df = df.dropna()
df
df = df[df['Content'] != 'No Match']
df
from sklearn.feature_extraction.text import CountVectorizer
cv = CountVectorizer(max_features=5000, stop_words='english')
vectors = cv.fit_transform(df['Content']).toarray()
vectors
cv.get_feature_names_out() | code |
130012258/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('/kaggle/input/midjourney-v5-prompts-and-links/MJ_Part3.csv')
df | code |
130012258/cell_23 | [
"text_html_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('/kaggle/input/midjourney-v5-prompts-and-links/MJ_Part3.csv')
df
df = df.drop(['AuthorID', 'Author', 'Date', 'Reactions', 'Attachments'], axis=1)
df
df = df.dropna()
df
df = df[df['Content'] != 'No Match']
df
from sklearn.feature_extraction.text import CountVectorizer
cv = CountVectorizer(max_features=5000, stop_words='english')
vectors = cv.fit_transform(df['Content']).toarray()
vectors | code |
130012258/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('/kaggle/input/midjourney-v5-prompts-and-links/MJ_Part3.csv')
df
df = df.drop(['AuthorID', 'Author', 'Date', 'Reactions', 'Attachments'], axis=1)
df | code |
130012258/cell_19 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('/kaggle/input/midjourney-v5-prompts-and-links/MJ_Part3.csv')
df
df = df.drop(['AuthorID', 'Author', 'Date', 'Reactions', 'Attachments'], axis=1)
df
df = df.dropna()
df
df = df[df['Content'] != 'No Match']
df
df | code |
130012258/cell_18 | [
"text_html_output_1.png"
] | from nltk.stem.porter import PorterStemmer
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('/kaggle/input/midjourney-v5-prompts-and-links/MJ_Part3.csv')
df
df = df.drop(['AuthorID', 'Author', 'Date', 'Reactions', 'Attachments'], axis=1)
df
df = df.dropna()
df
df = df[df['Content'] != 'No Match']
df
import nltk
from nltk.stem.porter import PorterStemmer
ps = PorterStemmer()
def stem(text):
y = []
for i in text.split():
y.append(ps.stem(i))
return ' '.join(y)
df['Content'] = df['Content'].apply(stem) | code |
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