path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
49124799/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/hackerearth-love-in-the-time-of-screens/data.csv')
df.drop(columns=['user_id', 'username'], inplace=True)
for i in df:
print(i, df[i].isna().sum()) | code |
49124799/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/hackerearth-love-in-the-time-of-screens/data.csv')
df.drop(columns=['user_id', 'username'], inplace=True)
df.shape
df.drop(columns=['language', 'bio'], inplace=True)
df.shape
a = df.dtypes
for i in a:
print(a[0]... | code |
49124799/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/hackerearth-love-in-the-time-of-screens/data.csv')
df.drop(columns=['user_id', 'username'], inplace=True)
len(df['language'].value_counts()) | code |
49124799/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/hackerearth-love-in-the-time-of-screens/data.csv')
df.drop(columns=['user_id', 'username'], inplace=True)
df.shape
df.drop(columns=['language', 'bio'], inplace=True)
df.shape | code |
49124799/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/hackerearth-love-in-the-time-of-screens/data.csv')
df.head() | code |
49124799/cell_35 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/hackerearth-love-in-the-time-of-screens/data.csv')
data = pd.DataFrame()
data.shape | code |
49124799/cell_31 | [
"text_html_output_1.png"
] | from sklearn.metrics.pairwise import cosine_similarity
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/hackerearth-love-in-the-time-of-screens/data.csv')
df.drop(columns=['user_id', 'username'], inplace=True)
df.shape
lan = []
for i in df['language']:
l = i.spl... | code |
49124799/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/hackerearth-love-in-the-time-of-screens/data.csv')
df.drop(columns=['user_id', 'username'], inplace=True)
df.shape
df.drop(columns=['language', 'bio'], inplace=True)
df.shape
a = df.dtypes
df.head() | code |
49124799/cell_37 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/hackerearth-love-in-the-time-of-screens/data.csv')
user_id = df['user_id']
data = pd.DataFrame()
data.shape
data.set_index(user_id, inplace=True)
data.head() | code |
2000829/cell_9 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train = train.drop(['PassengerId', 'Parch', 'Ticket', 'Cabin', 'Embarked'], axis=1)
test = test.drop(['Parch', 'Ticket', 'Cabin', 'Embarked'], axis=1)
combine = [train, test]
for dataset in combi... | code |
2000829/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train = train.drop(['PassengerId', 'Parch', 'Ticket', 'Cabin', 'Embarked'], axis=1)
test = test.drop(['Parch', 'Ticket', 'Cabin', 'Embarked'], axis=1)
combine = [train, test]
for dataset in combine:
dataset['Tit... | code |
2000829/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import warnings
import tensorflow as tf
import numpy as np
import pandas as pd
import seaborn as sns
import warnings
warnings.filterwarnings('ignore') | code |
2000829/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train = train.drop(['PassengerId', 'Parch', 'Ticket', 'Cabin', 'Embarked'], axis=1)
test = test.drop(['Parch', 'Ticket', 'Cabin', 'Embarked'], axis=1)
combine = [train, test]
for dataset in combi... | code |
2000829/cell_15 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import tensorflow as tf
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train = train.drop(['PassengerId', 'Parch', 'Ticket', 'Cabin', 'Embarked'], axis=1)
test = test.drop(['Parch', 'Ticket', 'Cabin', 'Embarked'], axis=1)
combine = [train, t... | code |
2000829/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train = train.drop(['PassengerId', 'Parch', 'Ticket', 'Cabin', 'Embarked'], axis=1)
test = test.drop(['Parch', 'Ticket', 'Cabin', 'Embarked'], axis=1)
test.describe(include='all') | code |
2000829/cell_12 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import tensorflow as tf
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train = train.drop(['PassengerId', 'Parch', 'Ticket', 'Cabin', 'Embarked'], axis=1)
test = test.drop(['Parch', 'Ticket', 'Cabin', 'Embarked'], axis=1)
combine = [train, t... | code |
128021577/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/email-spam-classification-dataset-csv/emails.csv')
df = df.drop(columns=['Email No.'])
df.shape
df.isna().sum()
df.duplicated().sum()
df = df.drop_duplicates()
df.duplicated().sum()
sns.countplot(data=df,... | code |
128021577/cell_9 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/email-spam-classification-dataset-csv/emails.csv')
df = df.drop(columns=['Email No.'])
df.shape
df.isna().sum() | code |
128021577/cell_34 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/email-spam-classification-dataset-csv/emails.csv')
df = df.drop(columns=['Email No.'])
df.shape
df.isna().sum()
df.duplicated().sum()
df = df.drop_duplicates()
df.duplicated().sum()
results = {}
plt.figu... | code |
128021577/cell_33 | [
"text_plain_output_1.png"
] | results = {}
results | code |
128021577/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
LR = LogisticRegression()
LR.fit(x_train, y_train)
LR_y_pred = LR.predict(x_test) | code |
128021577/cell_7 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/email-spam-classification-dataset-csv/emails.csv')
df = df.drop(columns=['Email No.'])
df.shape | code |
128021577/cell_8 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/email-spam-classification-dataset-csv/emails.csv')
df = df.drop(columns=['Email No.'])
df.shape
df.describe() | code |
128021577/cell_35 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/email-spam-classification-dataset-csv/emails.csv')
df = df.drop(columns=['Email No.'])
df.shape
df.isna().sum()
df.duplicated().sum()
df = df.drop_duplicates()
df.duplicated().sum()
results = {}
plt.ylim... | code |
128021577/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/email-spam-classification-dataset-csv/emails.csv')
df = df.drop(columns=['Email No.'])
df.shape
df.isna().sum()
df.duplicated().sum() | code |
128021577/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/email-spam-classification-dataset-csv/emails.csv')
df = df.drop(columns=['Email No.'])
df.shape
df.isna().sum()
df.duplicated().sum()
df = df.drop_duplicates()
df.duplicated().sum() | code |
128021577/cell_5 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/email-spam-classification-dataset-csv/emails.csv')
df.head() | code |
128021577/cell_36 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/email-spam-classification-dataset-csv/emails.csv')
df = df.drop(columns=['Email No.'])
df.shape
df.isna().sum()
df.duplicated().sum()
df = df.drop_duplicates()
df.duplicated().sum()
results = {}
plt.ylim... | code |
1007017/cell_6 | [
"text_plain_output_1.png"
] | import pandas
TRAIN_PATH = '../input/train.csv'
TEST_PATH = '../input/test.csv'
train = pandas.read_csv(TRAIN_PATH)
test = pandas.read_csv(TEST_PATH)
train.isnull().any() | code |
1007017/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
from sklearn.metrics import mean_absolute_error
age_rf = RandomForestRegressor()
age_rf.fit(age_train[['Pclass', 'encodedTitle', 'SibSpGroup1', 'SibSpGroup2', 'SibSpGroup3', 'ParChGT2']], age_train['Age'])
age_validation = age_validation.assig... | code |
1007017/cell_7 | [
"text_plain_output_1.png"
] | import pandas
TRAIN_PATH = '../input/train.csv'
TEST_PATH = '../input/test.csv'
train = pandas.read_csv(TRAIN_PATH)
test = pandas.read_csv(TEST_PATH)
test.isnull().any() | code |
1007017/cell_18 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
import pandas
import re
TRAIN_PATH = '../input/train.csv'
TEST_PATH = '../input/test.csv'
train = pandas.read_csv(TRAIN_PATH)
test = pandas.read_csv(TEST_PATH)
train.isnull().any()
test.isnull().any()
def deriveTitles(s):
title = re.search('(?:\\S )(?P<title>\\w*)', s).group(... | code |
1007017/cell_28 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn import preprocessing
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
import pandas
import re
TRAIN_PATH = '../input/train.csv'
TEST_PATH = ... | code |
2014045/cell_21 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.concat((df16, df17))
df2 = df[['teamAbbr', 'teamPTS', 'teamPTS1', 'teamPTS2', 'opptPTS', 'opptPTS1', 'opptPTS2']]
df2.loc[:, 'teamPTSH1'] = df2['teamPTS1'] + df['teamPTS2']
df2.loc[:, 'opptPTSH1'] = df2['opptPTS1'] + df['opptPTS2']
df2.loc[:, 'ptdiffH1'] = df2['teamPTSH... | code |
2014045/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.concat((df16, df17))
df2 = df[['teamAbbr', 'teamPTS', 'teamPTS1', 'teamPTS2', 'opptPTS', 'opptPTS1', 'opptPTS2']]
df2.loc[:, 'teamPTSH1'] = df2['teamPTS1'] + df['teamPTS2']
df2.loc[:, 'opptPTSH1'] = df2['opptPTS1'] + df['opptPTS2']
df2.loc[:, 'ptdiffH1'] = df2['teamPTSH... | code |
2014045/cell_9 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.concat((df16, df17))
df2 = df[['teamAbbr', 'teamPTS', 'teamPTS1', 'teamPTS2', 'opptPTS', 'opptPTS1', 'opptPTS2']]
df2.loc[:, 'teamPTSH1'] = df2['teamPTS1'] + df['teamPTS2']
df2.loc[:, 'opptPTSH1'] = df2['opptPTS1'] + df['opptPTS2']
df2.loc[:, 'ptdiffH1'] = df2['teamPTSH... | code |
2014045/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.concat((df16, df17))
df2 = df[['teamAbbr', 'teamPTS', 'teamPTS1', 'teamPTS2', 'opptPTS', 'opptPTS1', 'opptPTS2']]
df2.loc[:, 'teamPTSH1'] = df2['teamPTS1'] + df['teamPTS2']
df2.loc[:, 'opptPTSH1'] = df2['opptPTS1'] + df['opptPTS2']
df2.loc[:, 'ptdiffH1'] = df2['teamPTSH... | code |
2014045/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.concat((df16, df17))
df2 = df[['teamAbbr', 'teamPTS', 'teamPTS1', 'teamPTS2', 'opptPTS', 'opptPTS1', 'opptPTS2']]
df2.loc[:, 'teamPTSH1'] = df2['teamPTS1'] + df['teamPTS2']
df2.loc[:, 'opptPTSH1'] = df2['opptPTS1'] + df['opptPTS2']
df2.loc[:, 'ptdiffH1'] = df2['teamPTSH1'] - df2['opptPTSH1... | code |
2014045/cell_20 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.concat((df16, df17))
df2 = df[['teamAbbr', 'teamPTS', 'teamPTS1', 'teamPTS2', 'opptPTS', 'opptPTS1', 'opptPTS2']]
df2.loc[:, 'teamPTSH1'] = df2['teamPTS1'] + df['teamPTS2']
df2.loc[:, 'opptPTSH1'] = df2['opptPTS1'] + df['opptPTS2']
df2.loc[:, 'ptdiffH1'] = df2['teamPTSH... | code |
2014045/cell_29 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.concat((df16, df17))
df2 = df[['teamAbbr', 'teamPTS', 'teamPTS1', 'teamPTS2', 'opptPTS', 'opptPTS1', 'opptPTS2']]
df2.loc[:, 'teamPTSH1'] = df2['teamPTS1'] + df['teamPTS2']
df2.loc[:, 'opptPTSH1'] = df2['opptPTS1'] + df['opptPTS2']
df2.loc[:, 'ptdiffH1'] = df2['teamPTSH... | code |
2014045/cell_26 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.concat((df16, df17))
df2 = df[['teamAbbr', 'teamPTS', 'teamPTS1', 'teamPTS2', 'opptPTS', 'opptPTS1', 'opptPTS2']]
df2.loc[:, 'teamPTSH1'] = df2['teamPTS1'] + df['teamPTS2']
df2.loc[:, 'opptPTSH1'] = df2['opptPTS1'] + df['opptPTS2']
df2.l... | code |
2014045/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.concat((df16, df17))
df2 = df[['teamAbbr', 'teamPTS', 'teamPTS1', 'teamPTS2', 'opptPTS', 'opptPTS1', 'opptPTS2']]
df2.loc[:, 'teamPTSH1'] = df2['teamPTS1'] + df['teamPTS2']
df2.loc[:, 'opptPTSH1'] = df2['opptPTS1'] + df['opptPTS2']
df2.loc[:, 'ptdiffH1'] = df2['teamPTSH... | code |
2014045/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.concat((df16, df17))
df2 = df[['teamAbbr', 'teamPTS', 'teamPTS1', 'teamPTS2', 'opptPTS', 'opptPTS1', 'opptPTS2']]
df2.loc[:, 'teamPTSH1'] = df2['teamPTS1'] + df['teamPTS2']
df2.loc[:, 'opptPTSH1'] = df2['opptPTS1'] + df['opptPTS2']
df2.l... | code |
2014045/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.concat((df16, df17))
df2 = df[['teamAbbr', 'teamPTS', 'teamPTS1', 'teamPTS2', 'opptPTS', 'opptPTS1', 'opptPTS2']]
df2.loc[:, 'teamPTSH1'] = df2['teamPTS1'] + df['teamPTS2']
df2.loc[:, 'opptPTSH1'] = df2['opptPTS1'] + df['opptPTS2']
df2.loc[:, 'ptdiffH1'] = df2['teamPTSH... | code |
2014045/cell_18 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.concat((df16, df17))
df2 = df[['teamAbbr', 'teamPTS', 'teamPTS1', 'teamPTS2', 'opptPTS', 'opptPTS1', 'opptPTS2']]
df2.loc[:, 'teamPTSH1'] = df2['teamPTS1'] + df['teamPTS2']
df2.loc[:, 'opptPTSH1'] = df2['opptPTS1'] + df['opptPTS2']
df2.loc[:, 'ptdiffH1'] = df2['teamPTSH... | code |
2014045/cell_28 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.concat((df16, df17))
df2 = df[['teamAbbr', 'teamPTS', 'teamPTS1', 'teamPTS2', 'opptPTS', 'opptPTS1', 'opptPTS2']]
df2.loc[:, 'teamPTSH1'] = df2['teamPTS1'] + df['teamPTS2']
df2.loc[:, 'opptPTSH1'] = df2['opptPTS1'] + df['opptPTS2']
df2.loc[:, 'ptdiffH1'] = df2['teamPTSH... | code |
2014045/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.concat((df16, df17))
df2 = df[['teamAbbr', 'teamPTS', 'teamPTS1', 'teamPTS2', 'opptPTS', 'opptPTS1', 'opptPTS2']]
df2.loc[:, 'teamPTSH1'] = df2['teamPTS1'] + df['teamPTS2']
df2.loc[:, 'opptPTSH1'] = df2['opptPTS1'] + df['opptPTS2']
df2.l... | code |
2014045/cell_15 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.concat((df16, df17))
df2 = df[['teamAbbr', 'teamPTS', 'teamPTS1', 'teamPTS2', 'opptPTS', 'opptPTS1', 'opptPTS2']]
df2.loc[:, 'teamPTSH1'] = df2['teamPTS1'] + df['teamPTS2']
df2.loc[:, 'opptPTSH1'] = df2['opptPTS1'] + df['opptPTS2']
df2.loc[:, 'ptdiffH1'] = df2['teamPTSH... | code |
2014045/cell_22 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.concat((df16, df17))
df2 = df[['teamAbbr', 'teamPTS', 'teamPTS1', 'teamPTS2', 'opptPTS', 'opptPTS1', 'opptPTS2']]
df2.loc[:, 'teamPTSH1'] = df2['teamPTS1'] + df['teamPTS2']
df2.loc[:, 'opptPTSH1'] = df2['opptPTS1'] + df['opptPTS2']
df2.loc[:, 'ptdiffH1'] = df2['teamPTSH... | code |
2014045/cell_10 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.concat((df16, df17))
df2 = df[['teamAbbr', 'teamPTS', 'teamPTS1', 'teamPTS2', 'opptPTS', 'opptPTS1', 'opptPTS2']]
df2.loc[:, 'teamPTSH1'] = df2['teamPTS1'] + df['teamPTS2']
df2.loc[:, 'opptPTSH1'] = df2['opptPTS1'] + df['opptPTS2']
df2.loc[:, 'ptdiffH1'] = df2['teamPTSH... | code |
2014045/cell_27 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.concat((df16, df17))
df2 = df[['teamAbbr', 'teamPTS', 'teamPTS1', 'teamPTS2', 'opptPTS', 'opptPTS1', 'opptPTS2']]
df2.loc[:, 'teamPTSH1'] = df2['teamPTS1'] + df['teamPTS2']
df2.loc[:, 'opptPTSH1'] = df2['opptPTS1'] + df['opptPTS2']
df2.loc[:, 'ptdiffH1'] = df2['teamPTSH... | code |
2012289/cell_1 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
from scipy.stats import skew
from scipy.stats.stats import pearsonr
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train = train.drop(['Name'], axis=1)
test = test.drop(['Nam... | code |
2012289/cell_3 | [
"text_plain_output_1.png"
] | import matplotlib
import numpy as np
import pandas as pd
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
matplotlib.rcParams['figure.figsize'] = (14.0, 7.0)
prices = pd.DataFrame({'Fare': train['Fare'], 'log(Fare + 1)': np.log1p(train['Fare'])})
prices.hist() | code |
2012289/cell_10 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import skew
from sklearn.linear_model import LogisticRegression
import matplotlib
import numpy as np
import pandas as pd
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
matplotlib.rcParams['figure.figsize'] = (14.0, 7.0)
prices = pd.DataFrame({'Fare': t... | code |
2012289/cell_12 | [
"text_plain_output_1.png"
] | from scipy.stats import skew
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
import matplotlib
import numpy as np
import pandas as pd
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
matplotlib.rcParams['figure.fig... | code |
72112616/cell_9 | [
"text_html_output_1.png"
] | X_valid | code |
72112616/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
df_test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
cat_columns = [col for col in df_train.columns if df_train[col].dtype == 'object']
cat_columns | code |
72112616/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
df_test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
cat_columns = [col for col in df_train.columns if df_train[col].dtype == 'object']
low_cordinal_cols = [col for col in c... | code |
72112616/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 |
72112616/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
df_test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
cat_columns = [col for col in df_train.columns if df_train[col].dtype == 'object']
from sklearn.preprocessing import One... | code |
72112616/cell_18 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
df_test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
cat_columns = [col for col in df_train.columns if df_train[col].dtype ==... | code |
72112616/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
df_test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
cat_columns = [col for col in df_train.columns if df_train[col].dtype ==... | code |
72112616/cell_17 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.preprocessing import OneHotEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
df_test = pd.read_csv('/kaggle/inp... | code |
72112616/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
df_test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
cat_columns = [col for col in df_train.columns if df_train[col].dtype ==... | code |
72112616/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
df_test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
cat_columns = [col for col in df_train.columns if df_train[col].dtype ==... | code |
72112616/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
df_test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
cat_columns = [col for col in df_train.columns if df_train[col].dtype == 'object']
df_train[cat_columns].nunique() | code |
129039870/cell_6 | [
"text_plain_output_1.png"
] | !octave -W myinstall.m | code |
129039870/cell_2 | [
"text_plain_output_1.png"
] | !apt-get update
!apt --yes install octave
!apt-get install --yes liboctave-dev | code |
129039870/cell_8 | [
"text_plain_output_1.png"
] | !octave -W main.m | code |
34130462/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
metadata_df = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv', index_col='cord_uid')
example_df = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/target_tables/2_relevant_factors/How does temperature and humidity affect the transmission of 2019-nCoV.csv')
example_shas = []
... | code |
34130462/cell_23 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
metadata_df = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv', index_col='cord_uid')
example_df = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/target_tables/2_relevant_factors/How does temperature and humidity affect the transmission of 20... | code |
34130462/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
metadata_df = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv', index_col='cord_uid') | code |
34130462/cell_7 | [
"image_output_11.png",
"image_output_239.png",
"image_output_98.png",
"image_output_337.png",
"image_output_121.png",
"image_output_180.png",
"image_output_331.png",
"image_output_379.png",
"image_output_384.png",
"image_output_303.png",
"image_output_157.png",
"image_output_74.png",
"image_... | import pandas as pd
metadata_df = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv', index_col='cord_uid')
metadata_df | code |
34130462/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
metadata_df = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv', index_col='cord_uid')
example_df = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/target_tables/2_relevant_factors/How does temperature and humidity affect the transmission of 2019-nCoV.csv')
example_shas = []
... | code |
34130462/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
metadata_df = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv', index_col='cord_uid')
example_df = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/target_tables/2_relevant_factors/How does temperature and humidity affect the transmission of 2019-nCoV.csv')
example_shas = []
... | code |
34130462/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
metadata_df = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv', index_col='cord_uid')
example_df = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/target_tables/2_relevant_factors/How does temperature and humidity affect the transmission of 2019-nCoV.csv')
example_df | code |
32068850/cell_42 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sbm = pd.read_csv('/kaggle/input/college-salaries/degrees-that-pay-back.csv').set_index('Undergraduate Major')
sbc = pd.read_csv('/kaggle/input/college-salaries/salaries-by-college-type.csv').set_index('School Name')
sbr = pd.read_csv('/kaggle/inpu... | code |
32068850/cell_81 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sbm = pd.read_csv('/kaggle/input/college-salaries/degrees-that-pay-back.csv').set_index('Undergraduate Major')
sbc = pd.read_csv('/kaggle/input/college-salaries/salaries-by-college-type.csv').set_index('School Name')
sbr = pd.read_csv('/kaggle/inpu... | code |
32068850/cell_83 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sbm = pd.read_csv('/kaggle/input/college-salaries/degrees-that-pay-back.csv').set_index('Undergraduate Major')
sbc = pd.read_csv('/kaggle/input/college-salaries/salaries-by-college-type.csv').set_index('School Name')
sbr = pd.read_csv('/kaggle/inpu... | code |
32068850/cell_57 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sbm = pd.read_csv('/kaggle/input/college-salaries/degrees-that-pay-back.csv').set_index('Undergraduate Major')
sbc = pd.read_csv('/kaggle/input/college-salaries/salaries-by-college-type.csv').set_index('School Name')
sbr = pd.read_csv('/kaggle/inpu... | code |
32068850/cell_33 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sbm = pd.read_csv('/kaggle/input/college-salaries/degrees-that-pay-back.csv').set_index('Undergraduate Major')
sbc = pd.read_csv('/kaggle/input/college-salaries/salaries-by-college-type.csv').set_index('School Name')
sbr = pd.read_csv('/kaggle/inpu... | code |
32068850/cell_87 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sbm = pd.read_csv('/kaggle/input/college-salaries/degrees-that-pay-back.csv').set_index('Undergraduate Major')
sbc = pd.read_csv('/kaggle/input/college-salaries/salaries-by-college-type.csv').set_index('School Name')
sbr = pd.read_csv('/kaggle/inpu... | code |
32068850/cell_55 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sbm = pd.read_csv('/kaggle/input/college-salaries/degrees-that-pay-back.csv').set_index('Undergraduate Major')
sbc = pd.read_csv('/kaggle/input/college-salaries/salaries-by-college-type.csv').set_index('School Name')
sbr = pd.read_csv('/kaggle/inpu... | code |
32068850/cell_6 | [
"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 |
32068850/cell_76 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sbm = pd.read_csv('/kaggle/input/college-salaries/degrees-that-pay-back.csv').set_index('Undergraduate Major')
sbc = pd.read_csv('/kaggle/input/college-salaries/salaries-by-college-type.csv').set_index('School Name')
sbr = pd.read_csv('/kaggle/inpu... | code |
32068850/cell_39 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sbm = pd.read_csv('/kaggle/input/college-salaries/degrees-that-pay-back.csv').set_index('Undergraduate Major')
sbc = pd.read_csv('/kaggle/input/college-salaries/salaries-by-college-type.csv').set_index('School Name')
sbr = pd.read_csv('/kaggle/inpu... | code |
32068850/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sbm = pd.read_csv('/kaggle/input/college-salaries/degrees-that-pay-back.csv').set_index('Undergraduate Major')
sbc = pd.read_csv('/kaggle/input/college-salaries/salaries-by-college-type.csv').set_index('School Name')
sbr = pd.read_csv('/kaggle/inpu... | code |
32068850/cell_91 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sbm = pd.read_csv('/kaggle/input/college-salaries/degrees-that-pay-back.csv').set_index('Undergraduate Major')
sbc = pd.read_csv('/kaggle/input/college-salaries/salaries-by-college-type.csv').set_index('School Name')
sbr = pd.read_csv('/kaggle/inpu... | code |
32068850/cell_65 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sbm = pd.read_csv('/kaggle/input/college-salaries/degrees-that-pay-back.csv').set_index('Undergraduate Major')
sbc = pd.read_csv('/kaggle/input/college-salaries/salaries-by-college-type.csv').set_index('School Name')
sbr = pd.read_csv('/kaggle/inpu... | code |
32068850/cell_48 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sbm = pd.read_csv('/kaggle/input/college-salaries/degrees-that-pay-back.csv').set_index('Undergraduate Major')
sbc = pd.read_csv('/kaggle/input/college-salaries/salaries-by-college-type.csv').set_index('School Name')
sbr = pd.read_csv('/kaggle/inpu... | code |
32068850/cell_73 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sbm = pd.read_csv('/kaggle/input/college-salaries/degrees-that-pay-back.csv').set_index('Undergraduate Major')
sbc = pd.read_csv('/kaggle/input/college-salaries/salaries-by-college-type.csv').set_index('School Name')
sbr = pd.read_csv('/kaggle/inpu... | code |
32068850/cell_67 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sbm = pd.read_csv('/kaggle/input/college-salaries/degrees-that-pay-back.csv').set_index('Undergraduate Major')
sbc = pd.read_csv('/kaggle/input/college-salaries/salaries-by-college-type.csv').set_index('School Name')
sbr = pd.read_csv('/kaggle/inpu... | code |
32068850/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sbm = pd.read_csv('/kaggle/input/college-salaries/degrees-that-pay-back.csv').set_index('Undergraduate Major')
sbc = pd.read_csv('/kaggle/input/college-salaries/salaries-by-college-type.csv').set_index('School Name')
sbr = pd.read_csv('/kaggle/inpu... | code |
32068850/cell_69 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sbm = pd.read_csv('/kaggle/input/college-salaries/degrees-that-pay-back.csv').set_index('Undergraduate Major')
sbc = pd.read_csv('/kaggle/input/college-salaries/salaries-by-college-type.csv').set_index('School Name')
sbr = pd.read_csv('/kaggle/inpu... | code |
32068850/cell_52 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sbm = pd.read_csv('/kaggle/input/college-salaries/degrees-that-pay-back.csv').set_index('Undergraduate Major')
sbc = pd.read_csv('/kaggle/input/college-salaries/salaries-by-college-type.csv').set_index('School Name')
sbr = pd.read_csv('/kaggle/inpu... | code |
32068850/cell_49 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sbm = pd.read_csv('/kaggle/input/college-salaries/degrees-that-pay-back.csv').set_index('Undergraduate Major')
sbc = pd.read_csv('/kaggle/input/college-salaries/salaries-by-college-type.csv').set_index('School Name')
sbr = pd.read_csv('/kaggle/inpu... | code |
32068850/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sbm = pd.read_csv('/kaggle/input/college-salaries/degrees-that-pay-back.csv').set_index('Undergraduate Major')
sbc = pd.read_csv('/kaggle/input/college-salaries/salaries-by-college-type.csv').set_index('School Name')
sbr = pd.read_csv('/kaggle/inpu... | code |
32068850/cell_89 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sbm = pd.read_csv('/kaggle/input/college-salaries/degrees-that-pay-back.csv').set_index('Undergraduate Major')
sbc = pd.read_csv('/kaggle/input/college-salaries/salaries-by-college-type.csv').set_index('School Name')
sbr = pd.read_csv('/kaggle/inpu... | code |
32068850/cell_51 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sbm = pd.read_csv('/kaggle/input/college-salaries/degrees-that-pay-back.csv').set_index('Undergraduate Major')
sbc = pd.read_csv('/kaggle/input/college-salaries/salaries-by-college-type.csv').set_index('School Name')
sbr = pd.read_csv('/kaggle/inpu... | code |
32068850/cell_62 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sbm = pd.read_csv('/kaggle/input/college-salaries/degrees-that-pay-back.csv').set_index('Undergraduate Major')
sbc = pd.read_csv('/kaggle/input/college-salaries/salaries-by-college-type.csv').set_index('School Name')
sbr = pd.read_csv('/kaggle/inpu... | code |
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