path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
128003343/cell_41 | [
"text_plain_output_1.png"
] | import pandas as pd
SHEET_ID = '10DM_ZqwV6CvOryD6u3pYHb5scjEfiwfkitUGTCG8l7Y'
SHEET_NAME = 'AAPL'
url = f'https://docs.google.com/spreadsheets/d/{SHEET_ID}/gviz/tq?tqx=out:csv&sheet={SHEET_NAME}'
df = pd.read_csv(url, decimal=',')
df_retention_ab = df.groupby('version').agg({'userid': 'count', 'retention_1': 'mean', ... | code |
128003343/cell_52 | [
"text_plain_output_1.png"
] | from statsmodels.stats import proportion
import math
import numpy as np
import pandas as pd
SHEET_ID = '10DM_ZqwV6CvOryD6u3pYHb5scjEfiwfkitUGTCG8l7Y'
SHEET_NAME = 'AAPL'
url = f'https://docs.google.com/spreadsheets/d/{SHEET_ID}/gviz/tq?tqx=out:csv&sheet={SHEET_NAME}'
df = pd.read_csv(url, decimal=',')
df_retention... | code |
128003343/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
SHEET_ID = '10DM_ZqwV6CvOryD6u3pYHb5scjEfiwfkitUGTCG8l7Y'
SHEET_NAME = 'AAPL'
url = f'https://docs.google.com/spreadsheets/d/{SHEET_ID}/gviz/tq?tqx=out:csv&sheet={SHEET_NAME}'
df = pd.read_csv(url, decimal=',')
df.info() | code |
128003343/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
SHEET_ID = '10DM_ZqwV6CvOryD6u3pYHb5scjEfiwfkitUGTCG8l7Y'
SHEET_NAME = 'AAPL'
url = f'https://docs.google.com/spreadsheets/d/{SHEET_ID}/gviz/tq?tqx=out:csv&sheet={SHEET_NAME}'
df = pd.read_csv(url, decimal=',')
df_retention_ab = df.groupby('v... | code |
128003343/cell_32 | [
"image_output_1.png"
] | import pandas as pd
SHEET_ID = '10DM_ZqwV6CvOryD6u3pYHb5scjEfiwfkitUGTCG8l7Y'
SHEET_NAME = 'AAPL'
url = f'https://docs.google.com/spreadsheets/d/{SHEET_ID}/gviz/tq?tqx=out:csv&sheet={SHEET_NAME}'
df = pd.read_csv(url, decimal=',')
df_retention_ab = df.groupby('version').agg({'userid': 'count', 'retention_1': 'mean', ... | code |
128003343/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
SHEET_ID = '10DM_ZqwV6CvOryD6u3pYHb5scjEfiwfkitUGTCG8l7Y'
SHEET_NAME = 'AAPL'
url = f'https://docs.google.com/spreadsheets/d/{SHEET_ID}/gviz/tq?tqx=out:csv&sheet={SHEET_NAME}'
df = pd.read_csv(url, decimal=',')
df.describe() | code |
128003343/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
SHEET_ID = '10DM_ZqwV6CvOryD6u3pYHb5scjEfiwfkitUGTCG8l7Y'
SHEET_NAME = 'AAPL'
url = f'https://docs.google.com/spreadsheets/d/{SHEET_ID}/gviz/tq?tqx=out:csv&sheet={SHEET_NAME}'
df = pd.read_csv(url, decimal=',')
df_retention_ab = df.groupby('version').agg({'userid': 'count', 'retention_1': 'mean', ... | code |
128003343/cell_47 | [
"text_plain_output_1.png"
] | import pandas as pd
SHEET_ID = '10DM_ZqwV6CvOryD6u3pYHb5scjEfiwfkitUGTCG8l7Y'
SHEET_NAME = 'AAPL'
url = f'https://docs.google.com/spreadsheets/d/{SHEET_ID}/gviz/tq?tqx=out:csv&sheet={SHEET_NAME}'
df = pd.read_csv(url, decimal=',')
df_retention_ab = df.groupby('version').agg({'userid': 'count', 'retention_1': 'mean', ... | code |
128003343/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
SHEET_ID = '10DM_ZqwV6CvOryD6u3pYHb5scjEfiwfkitUGTCG8l7Y'
SHEET_NAME = 'AAPL'
url = f'https://docs.google.com/spreadsheets/d/{SHEET_ID}/gviz/tq?tqx=out:csv&sheet={SHEET_NAME}'
df = pd.read_csv(url, decimal=',')
df_retention_ab = df.groupby('version').agg({'userid': 'count', 'retention_1': 'mean', ... | code |
128003343/cell_43 | [
"text_plain_output_1.png"
] | from statsmodels.stats import proportion
import numpy as np
import pandas as pd
SHEET_ID = '10DM_ZqwV6CvOryD6u3pYHb5scjEfiwfkitUGTCG8l7Y'
SHEET_NAME = 'AAPL'
url = f'https://docs.google.com/spreadsheets/d/{SHEET_ID}/gviz/tq?tqx=out:csv&sheet={SHEET_NAME}'
df = pd.read_csv(url, decimal=',')
df_retention_ab = df.grou... | code |
128003343/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd
SHEET_ID = '10DM_ZqwV6CvOryD6u3pYHb5scjEfiwfkitUGTCG8l7Y'
SHEET_NAME = 'AAPL'
url = f'https://docs.google.com/spreadsheets/d/{SHEET_ID}/gviz/tq?tqx=out:csv&sheet={SHEET_NAME}'
df = pd.read_csv(url, decimal=',')
df_retention_ab = df.groupby('version').agg({'userid': 'count', 'retention_1': 'mean', ... | code |
128003343/cell_24 | [
"text_plain_output_1.png"
] | from scipy.stats import ttest_1samp, mannwhitneyu, shapiro, norm, t, kstest, shapiro
import pandas as pd
SHEET_ID = '10DM_ZqwV6CvOryD6u3pYHb5scjEfiwfkitUGTCG8l7Y'
SHEET_NAME = 'AAPL'
url = f'https://docs.google.com/spreadsheets/d/{SHEET_ID}/gviz/tq?tqx=out:csv&sheet={SHEET_NAME}'
df = pd.read_csv(url, decimal=',')
d... | code |
128003343/cell_27 | [
"text_html_output_1.png"
] | from scipy.stats import ttest_1samp, mannwhitneyu, shapiro, norm, t, kstest, shapiro
import pandas as pd
SHEET_ID = '10DM_ZqwV6CvOryD6u3pYHb5scjEfiwfkitUGTCG8l7Y'
SHEET_NAME = 'AAPL'
url = f'https://docs.google.com/spreadsheets/d/{SHEET_ID}/gviz/tq?tqx=out:csv&sheet={SHEET_NAME}'
df = pd.read_csv(url, decimal=',')
d... | code |
128003343/cell_37 | [
"text_plain_output_1.png"
] | if abs(pvalue) < 0.05:
print('We may reject the null hypothesis!')
else:
print('We have failed to reject the null hypothesis') | code |
128003343/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
SHEET_ID = '10DM_ZqwV6CvOryD6u3pYHb5scjEfiwfkitUGTCG8l7Y'
SHEET_NAME = 'AAPL'
url = f'https://docs.google.com/spreadsheets/d/{SHEET_ID}/gviz/tq?tqx=out:csv&sheet={SHEET_NAME}'
df = pd.read_csv(url, decimal=',')
df['userid'].nunique() == df['userid'].count() | code |
128003343/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
SHEET_ID = '10DM_ZqwV6CvOryD6u3pYHb5scjEfiwfkitUGTCG8l7Y'
SHEET_NAME = 'AAPL'
url = f'https://docs.google.com/spreadsheets/d/{SHEET_ID}/gviz/tq?tqx=out:csv&sheet={SHEET_NAME}'
df = pd.read_csv(url, decimal=',')
df.head(10) | code |
128003343/cell_36 | [
"text_plain_output_1.png"
] | from statsmodels.stats import proportion
import numpy as np
import pandas as pd
SHEET_ID = '10DM_ZqwV6CvOryD6u3pYHb5scjEfiwfkitUGTCG8l7Y'
SHEET_NAME = 'AAPL'
url = f'https://docs.google.com/spreadsheets/d/{SHEET_ID}/gviz/tq?tqx=out:csv&sheet={SHEET_NAME}'
df = pd.read_csv(url, decimal=',')
df_retention_ab = df.grou... | code |
330371/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv', index_col='PassengerId')
df.dropna().describe()
df['Parch'].value_counts(sort=False) | code |
330371/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv', index_col='PassengerId')
df.dropna().describe()
df['Sex'].value_counts() | code |
330371/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv', index_col='PassengerId')
df.head() | code |
330371/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv', index_col='PassengerId')
df.dropna().describe()
df['Fare'].value_counts().head(20) | code |
330371/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv', index_col='PassengerId')
df.info() | code |
330371/cell_40 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv', index_col='PassengerId')
df.dropna().describe()
ages_mean = df.pivot_table('Age', index=['Title'], columns=['Sex', 'Pclass'], aggfunc='mean')
ages_mean
ages_std = df.pivot_table('Age', index=['Title'], columns=['Sex', 'Pclass'], aggfunc='std')
ages_std | code |
330371/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv', index_col='PassengerId')
df.dropna().describe()
df['Cabin'].value_counts(dropna=False)[:20] | code |
330371/cell_39 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv', index_col='PassengerId')
df.dropna().describe()
ages_mean = df.pivot_table('Age', index=['Title'], columns=['Sex', 'Pclass'], aggfunc='mean')
ages_mean | code |
330371/cell_41 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv', index_col='PassengerId')
df.dropna().describe()
ages_mean = df.pivot_table('Age', index=['Title'], columns=['Sex', 'Pclass'], aggfunc='mean')
ages_mean
ages_std = df.pivot_table('Age', index=['Title'], columns=['Sex', 'Pclass'], aggfunc=... | code |
330371/cell_54 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv', index_col='PassengerId')
df.dropna().describe()
ages_mean = df.pivot_table('Age', index=['Title'], columns=['Sex', 'Pclass'], aggfunc='mean')
ages_mean
ages_std = df.pivot_table('Age', index=['Title'], columns=['Sex', 'Pclass'], aggfunc=... | code |
330371/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv', index_col='PassengerId')
df.dropna().describe()
df['SibSp'].value_counts(sort=False) | code |
330371/cell_50 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv', index_col='PassengerId')
df.dropna().describe()
ages_mean = df.pivot_table('Age', index=['Title'], columns=['Sex', 'Pclass'], aggfunc='mean')
ages_mean
ages_std = df.pivot_table('Age', index=['Title'], columns=['Sex', 'Pclass'], aggfunc=... | code |
330371/cell_52 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/train.csv', index_col='PassengerId')
df.dropna().describe()
ages_mean = df.pivot_table('Age', index=['Title'], columns=['Sex', 'Pclass'], aggfunc='mean')
ages_mean
ages_std = df.pivot_table('Ag... | code |
330371/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv', index_col='PassengerId')
df.dropna().describe() | code |
330371/cell_45 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/train.csv', index_col='PassengerId')
df.dropna().describe()
ages_mean = df.pivot_table('Age', index=['Title'], columns=['Sex', 'Pclass'], aggfunc='mean')
ages_mean
ages_std = df.pivot_table('Ag... | code |
330371/cell_49 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv', index_col='PassengerId')
df.dropna().describe()
ages_mean = df.pivot_table('Age', index=['Title'], columns=['Sex', 'Pclass'], aggfunc='mean')
ages_mean
ages_std = df.pivot_table('Age', index=['Title'], columns=['Sex', 'Pclass'], aggfunc=... | code |
330371/cell_51 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/train.csv', index_col='PassengerId')
df.dropna().describe()
ages_mean = df.pivot_table('Age', index=['Title'], columns=['Sex', 'Pclass'], aggfunc='mean')
ages_mean
ages_std = df.pivot_table('Ag... | code |
330371/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv', index_col='PassengerId')
df.dropna().describe()
df.select_dtypes(include=['object']).describe() | code |
330371/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv', index_col='PassengerId')
df.dropna().describe()
df['Age'].value_counts(dropna=False)[:20] | code |
330371/cell_47 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/train.csv', index_col='PassengerId')
df.dropna().describe()
ages_mean = df.pivot_table('Age', index=['Title'], columns=['Sex', 'Pclass'], aggfunc='mean')
ages_mean
ages_std = df.pivot_table('Ag... | code |
330371/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv', index_col='PassengerId')
df.dropna().describe()
df['Age'].hist(bins=20) | code |
330371/cell_35 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/train.csv', index_col='PassengerId')
df.dropna().describe()
sns.factorplot(x='Sex', y='Survived', data=df, kind='bar', size=5, ci=None)
plt.title('Survival Rate by Gender') | code |
330371/cell_43 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv', index_col='PassengerId')
df.dropna().describe()
ages_mean = df.pivot_table('Age', index=['Title'], columns=['Sex', 'Pclass'], aggfunc='mean')
ages_mean
ages_std = df.pivot_table('Age', index=['Title'], columns=['Sex', 'Pclass'], aggfunc=... | code |
330371/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv', index_col='PassengerId')
df.dropna().describe()
df['Embarked'].value_counts(dropna=False) | code |
330371/cell_53 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/train.csv', index_col='PassengerId')
df.dropna().describe()
ages_mean = df.pivot_table('Age', index=['Title'], columns=['Sex', 'Pclass'], aggfunc='mean')
ages_mean
ages_std = df.pivot_table('Ag... | code |
330371/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv', index_col='PassengerId')
df.dropna().describe()
df['Survived'].value_counts() | code |
330371/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv', index_col='PassengerId')
df.dropna().describe()
df['Ticket'].value_counts()[:20] | code |
330371/cell_37 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/train.csv', index_col='PassengerId')
df.dropna().describe()
sns.factorplot(x='Pclass', y='Survived', hue='Sex', data=df, kind='bar', size=5, aspect=1.5, ci=None)
plt.title('Survival Rate by Class and Gender') | code |
330371/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv', index_col='PassengerId')
df.dropna().describe()
df['Pclass'].value_counts(sort=False) | code |
330371/cell_36 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/train.csv', index_col='PassengerId')
df.dropna().describe()
sns.factorplot(x='Pclass', y='Survived', data=df, kind='bar', size=5, ci=None)
plt.title('Survival Rate by Class') | code |
1007016/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | d = pd.read_csv('../input/train.csv')
t = pd.read_csv('../input/test.csv')
X_t = t.loc[:, ['Class1', 'Class2', 'Male', 'Age', 'SibSp', 'Parch', 'Fare']] | code |
1007016/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | d = pd.read_csv('../input/train.csv')
d['Male'] = d['Sex'] == 'male'
n = d['Age'].mean()
d['Class1'] = d['Pclass'] == 1
d['Class2'] = d['Pclass'] == 2
d['Age'].fillna(n, inplace=True) | code |
1007016/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | d = pd.read_csv('../input/train.csv')
d.head() | code |
1007016/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | d = pd.read_csv('../input/train.csv')
t = pd.read_csv('../input/test.csv') | code |
1007016/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | d = pd.read_csv('../input/train.csv') | code |
1007016/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier
d = pd.read_csv('../input/train.csv')
X = d.loc[:, ['Class1', 'Class2', 'Male', 'Age', 'SibSp', 'Parch', 'Fare']]
y = d['Survived']
thisclf = DecisionTreeClassifier()
thisclf.fit(X, y) | code |
1007016/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier
d = pd.read_csv('../input/train.csv')
X = d.loc[:, ['Class1', 'Class2', 'Male', 'Age', 'SibSp', 'Parch', 'Fare']]
y = d['Survived']
thisclf = DecisionTreeClassifier()
thisclf.fit(X, y)
d['predicted'] = thisclf.predict(X) | code |
1007016/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png"
] | d = pd.read_csv('../input/train.csv')
t = pd.read_csv('../input/test.csv')
X_t = t.loc[:, ['Class1', 'Class2', 'Male', 'Age', 'SibSp', 'Parch', 'Fare']]
t_out = t.loc[:, ['PassengerId', 'Survived']] | code |
1007016/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png"
] | d = pd.read_csv('../input/train.csv')
t = pd.read_csv('../input/test.csv')
X_t = t.loc[:, ['Class1', 'Class2', 'Male', 'Age', 'SibSp', 'Parch', 'Fare']]
t_out = t.loc[:, ['PassengerId', 'Survived']]
t_out.to_csv('out.csv') | code |
1007016/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier
d = pd.read_csv('../input/train.csv')
X = d.loc[:, ['Class1', 'Class2', 'Male', 'Age', 'SibSp', 'Parch', 'Fare']]
y = d['Survived']
thisclf = DecisionTreeClassifier()
thisclf.fit(X, y)
d['predicted'] = thisclf.predict(X)
t = pd.read_csv('../input/test.csv')
X_t = t... | code |
1007016/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.metrics import accuracy_score
d = pd.read_csv('../input/train.csv')
X = d.loc[:, ['Class1', 'Class2', 'Male', 'Age', 'SibSp', 'Parch', 'Fare']]
y = d['Survived']
accuracy_score(y, d['predicted']) | code |
1007016/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | d = pd.read_csv('../input/train.csv')
t = pd.read_csv('../input/test.csv')
t['Male'] = t['Sex'] == 'male'
nn = t['Age'].mean()
t['Class1'] = t['Pclass'] == 1
t['Class2'] = t['Pclass'] == 2
t['Age'].fillna(nn, inplace=True)
f = t['Fare'].mean()
t['Fare'].fillna(f, inplace=True) | code |
1007016/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | d = pd.read_csv('../input/train.csv')
X = d.loc[:, ['Class1', 'Class2', 'Male', 'Age', 'SibSp', 'Parch', 'Fare']]
y = d['Survived'] | code |
73084725/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/seoul-bike-rental-ai-pro-iti/train.csv')
df_train.isna().sum()
df_train.hist() | code |
73084725/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/seoul-bike-rental-ai-pro-iti/train.csv')
df_train.isna().sum()
y = df_train['y'].copy()
df_train.drop(['y'], axis='columns', inplace=True)
df_train['y'] = y
df_train.columns | code |
73084725/cell_33 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/seoul-bike-rental-ai-pro-iti/train.csv')
df_test = pd.read_csv('/kaggle/input/seoul-bike-rental-ai-pro-iti/test.csv')
df_test
df_train.isna().sum()
df_test.isna().sum()
table = pd.cross... | code |
73084725/cell_44 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/seoul-bike-rental-ai-pro-iti/train.csv')
df_test = pd.read_csv('/kaggle/input/seoul-bike-rental-ai-pro-iti/test.csv')
df_test
df_train.isna().sum()
df_test.isna().sum()
table = pd.cross... | code |
73084725/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/seoul-bike-rental-ai-pro-iti/train.csv')
df_train.describe() | code |
73084725/cell_40 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/seoul-bike-rental-ai-pro-iti/train.csv')
df_test = pd.read_csv('/kaggle/input/seoul-bike-rental-ai-pro-iti/test.csv')
df_test
df_train.isna().sum()
df_test.isna().sum()
table = pd.cross... | code |
73084725/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/seoul-bike-rental-ai-pro-iti/train.csv')
df_test = pd.read_csv('/kaggle/input/seoul-bike-rental-ai-pro-iti/test.csv')
df_test
df_train.isna().sum()
df_test.isna().sum()
table = pd.cross... | code |
73084725/cell_26 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/seoul-bike-rental-ai-pro-iti/train.csv')
df_test = pd.read_csv('/kaggle/input/seoul-bike-rental-ai-pro-iti/test.csv')
df_test
df_train.isna().sum()
df_test.isna().sum()
table = pd.cross... | code |
73084725/cell_48 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/seoul-bike-rental-ai-pro-iti/train.csv')
df_test = pd.read_csv('/kaggle/input/seoul-bike-rental-ai-pro-iti/test.csv')
df_test
df_train.isna().sum()
df_test.isna().sum()
table = pd.cross... | code |
73084725/cell_54 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/seoul-bike-rental-ai-pro-iti/train.csv')
df_test = pd.read_csv('/kaggle/input/seoul-bike-rental-ai-pro-iti/test.csv')
df_test
df_train.isna().sum()
df_test.isna().sum()
table = pd.cross... | code |
73084725/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/seoul-bike-rental-ai-pro-iti/train.csv')
df_train.isna().sum() | code |
73084725/cell_7 | [
"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)
df_train = pd.read_csv('/kaggle/input/seoul-bike-rental-ai-pro-iti/train.csv')
df_train.describe() | code |
73084725/cell_45 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/seoul-bike-rental-ai-pro-iti/train.csv')
df_test = pd.read_csv('/kaggle/input/seoul-bike-rental-ai-pro-iti/test.csv')
df_test
df_train.isna().sum()
df_test.isna().sum()
table = pd.cross... | code |
73084725/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/seoul-bike-rental-ai-pro-iti/train.csv')
df_train.isna().sum()
df_train.describe() | code |
73084725/cell_32 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/seoul-bike-rental-ai-pro-iti/train.csv')
df_test = pd.read_csv('/kaggle/input/seoul-bike-rental-ai-pro-iti/test.csv')
df_test
df_train.isna().sum()
df_test.isna().sum()
table = pd.cross... | code |
73084725/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)
df_train = pd.read_csv('/kaggle/input/seoul-bike-rental-ai-pro-iti/train.csv')
df_test = pd.read_csv('/kaggle/input/seoul-bike-rental-ai-pro-iti/test.csv')
df_test | code |
73084725/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/seoul-bike-rental-ai-pro-iti/train.csv')
df_test = pd.read_csv('/kaggle/input/seoul-bike-rental-ai-pro-iti/test.csv')
df_test
df_train.isna().sum()
table = pd.crosstab(df_train['Holiday'... | code |
73084725/cell_47 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/seoul-bike-rental-ai-pro-iti/train.csv')
df_test = pd.read_csv('/kaggle/input/seoul-bike-rental-ai-pro-iti/test.csv')
df_test
df_train.isna().sum()
df_test.isna().sum()
table = pd.cross... | code |
73084725/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/seoul-bike-rental-ai-pro-iti/train.csv')
df_test = pd.read_csv('/kaggle/input/seoul-bike-rental-ai-pro-iti/test.csv')
df_test
df_train.isna().sum()
df_test.isna().sum()
table = pd.cross... | code |
73084725/cell_46 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/seoul-bike-rental-ai-pro-iti/train.csv')
df_test = pd.read_csv('/kaggle/input/seoul-bike-rental-ai-pro-iti/test.csv')
df_test
df_train.isna().sum()
df_test.isna().sum()
table = pd.cross... | code |
73084725/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/seoul-bike-rental-ai-pro-iti/train.csv')
df_test = pd.read_csv('/kaggle/input/seoul-bike-rental-ai-pro-iti/test.csv')
df_test
df_test.isna().sum()
df_test.columns | code |
73084725/cell_27 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/seoul-bike-rental-ai-pro-iti/train.csv')
df_test = pd.read_csv('/kaggle/input/seoul-bike-rental-ai-pro-iti/test.csv')
df_test
df_train.isna().sum()
df_test.isna().sum()
table = pd.cross... | code |
73084725/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/seoul-bike-rental-ai-pro-iti/train.csv')
df_test = pd.read_csv('/kaggle/input/seoul-bike-rental-ai-pro-iti/test.csv')
df_test
df_test.isna().sum() | code |
73084725/cell_5 | [
"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 |
128039471/cell_21 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('/kaggle/input/students-performance-in-exams/StudentsPerformance.csv')
df.count()
plt.hist(df[(df['test preparation course'] == 'none') & (df['gender'] == 'male')]['math score'], label='Male-None')
plt.hist(df[(df['test preparation course'] == 'co... | code |
128039471/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('/kaggle/input/students-performance-in-exams/StudentsPerformance.csv')
df.count()
plt.hist(df[df['gender'] == 'male']['math score'], label='male')
plt.legend()
plt.show() | code |
128039471/cell_20 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('/kaggle/input/students-performance-in-exams/StudentsPerformance.csv')
df.count()
plt.hist(df[df['test preparation course'] == 'none']['math score'], label='All-None')
plt.hist(df[df['test preparation course'] == 'completed']['math score'], label=... | code |
128039471/cell_6 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/students-performance-in-exams/StudentsPerformance.csv')
df.count()
total_count = len(df)
total_count | code |
128039471/cell_7 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/students-performance-in-exams/StudentsPerformance.csv')
df.count()
men_count = len(df[df['gender'] == 'male'])
female_count = len(df[df['gender'] == 'female'])
print('men_count:', men_count)
print('female_count:', female_count) | code |
128039471/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/students-performance-in-exams/StudentsPerformance.csv')
df.count()
avg_all_test_completed = df[df['test preparation course'] == 'completed']['math score'].mean()
avg_all_test_none = df[df['test preparation course'] == 'none']['math score'].mean()
print('avg_all_tes... | code |
128039471/cell_8 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/students-performance-in-exams/StudentsPerformance.csv')
df.count()
total_count = len(df)
total_count
men_count = len(df[df['gender'] == 'male'])
female_count = len(df[df['gender'] == 'female'])
proportion_men = men_count / total_count
proportion_female = female_c... | code |
128039471/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/students-performance-in-exams/StudentsPerformance.csv')
df.count()
std_ms_all = df['math score'].std()
std_ms_men = df[df['gender'] == 'male']['math score'].std()
std_ms_female = df[df['gender'] == 'female']['math score'].std()
print('std_ms_all:', std_ms_all)
prin... | code |
128039471/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/students-performance-in-exams/StudentsPerformance.csv')
df.head() | code |
128039471/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('/kaggle/input/students-performance-in-exams/StudentsPerformance.csv')
df.count()
plt.hist(df[df['gender'] == 'male']['math score'], label='female')
plt.legend()
plt.show() | code |
128039471/cell_22 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('/kaggle/input/students-performance-in-exams/StudentsPerformance.csv')
df.count()
plt.hist(df[(df['test preparation course'] == 'none') & (df['gender'] == 'female')]['math score'], label='Female-None')
plt.hist(df[(df['test preparation course'] ==... | code |
128039471/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/students-performance-in-exams/StudentsPerformance.csv')
df.count()
avg_ms_all = df['math score'].mean()
avg_ms_men = df[df['gender'] == 'male']['math score'].mean()
avg_ms_female = df[df['gender'] == 'female']['math score'].mean()
print('avg_ms_all:', avg_ms_all)
p... | code |
128039471/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('/kaggle/input/students-performance-in-exams/StudentsPerformance.csv')
df.count()
plt.hist(df['math score'], label='ALL')
plt.legend()
plt.show() | code |
128039471/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/students-performance-in-exams/StudentsPerformance.csv')
df.count() | code |
34150890/cell_2 | [
"text_plain_output_1.png"
] | import os
import os
import numpy as np
import pandas as pd
import os
import numpy as np
import pandas as pd
import os
import keras
from keras.layers import Conv2D, MaxPool2D, Flatten, Dense, Dropout, BatchNormalization
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from ... | code |
34150890/cell_11 | [
"text_plain_output_1.png"
] | from keras import layers
from keras import models
from keras.applications import VGG16
from sklearn.model_selection import train_test_split
import cv2
import keras
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import os
import os
import numpy as np
import pandas as pd... | code |
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