path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
130007434/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
hr.columns
hr.isna().sum()
dupl... | code |
130007434/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
hr.columns
hr.isna().sum() | code |
130007434/cell_20 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('... | code |
130007434/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.describe() | code |
130007434/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
hr.columns
hr.isna().sum()
dupl... | code |
130007434/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
pd.set_option('display.max_columns', None)
from xgboost import XGBClassifier
from xgboost import XGBRegressor
from xgboost import plot_importance
from sklearn.linear_model import LogisticRegression
from sklearn.tree import Deci... | code |
130007434/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns | code |
130007434/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
... | code |
130007434/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
hr.columns | code |
130007434/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
... | code |
130007434/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
... | code |
130007434/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.head(10) | code |
130007434/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
... | code |
130007434/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
hr.columns
hr.isna().sum()
dupl... | code |
130007434/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
hr.columns
hr.isna().sum()
dupl... | code |
130007434/cell_5 | [
"image_output_1.png"
] | import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.info() | code |
130012889/cell_6 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train) | code |
130012889/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.impute import SimpleImputer
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score | code |
130012889/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
preds_valid = model.predict(X_valid)
print(accuracy_score(y_valid, preds_valid)) | code |
18148304/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import statsmodels.api as sm # https://www.statsmodels.org/stable/index.html
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
input_data = pd.read_csv('../input/US_graduate_schools_admission_parameters_dataset_private.csv')
X = input_data[['GRE Score', 'IELTS Score... | code |
18148304/cell_6 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt # documentation: https://matplotlib.org/api/pyplot_api.html
import pandas as pd
import statsmodels.api as sm # https://www.statsmodels.org/stable/index.html
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
input_data = pd.read_csv('../input/US_graduat... | code |
18148304/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import statsmodels.api as sm # https://www.statsmodels.org/stable/index.html
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
input_data = pd.read_csv('../input/US_graduate_schools_admission_parameters_dataset_private.csv')
X = input_data[['GRE Score', 'IELTS Score... | code |
18148304/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
input_data = pd.read_csv('../input/US_graduate_schools_admission_parameters_dataset_private.csv')
input_data.head() | code |
33109004/cell_25 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
car_us_df = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
special_cases_price_zero = car_us_df[car_us_df.price == 0]
special_cases_price_zero.shape
special_cases_price_great = car_us_df[ca... | code |
33109004/cell_56 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split,cross_val_score,KFold,cross_val_predict,GridSearchCV
from xgboost import XGBRegressor
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
def plot_bar_vertical(df, figsize=(10, 15), xlabel=... | code |
33109004/cell_33 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
def plot_bar_vertical(df, figsize=(10, 15), xlabel='Count Number'):
ax = df.plot.barh(figsize=figsize)
for p in ax.patches:
ax.text(p.get_x() + p.get_width(), p.get_y() + p.get... | code |
33109004/cell_44 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
def plot_bar_vertical(df, figsize=(10, 15), xlabel='Count Number'):
ax = df.plot.barh(figsize=figsize)
for p in ax.patches:
ax.text(p.get_x() + p.get_width(), p.get_y() + p.get... | code |
33109004/cell_55 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
def plot_bar_vertical(df, figsize=(10, 15), xlabel='Count Number'):
ax = df.plot.barh(figsize=figsize)
for p in ax.patches:
ax.text(p.get_x() + p.get_width(), p.get_y() + p.get... | code |
33109004/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
car_us_df = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
car_us_df.info() | code |
33109004/cell_40 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
def plot_bar_vertical(df, figsize=(10, 15), xlabel='Count Number'):
ax = df.plot.barh(figsize=figsize)
for p in ax.patches:
ax.text(p.get_x() + p.get_width(), p.get_y() + p.get... | code |
33109004/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
car_us_df = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
special_cases_price_zero = car_us_df[car_us_df.price == 0]
special_cases_price_zero.shape
special_cases_price_great = car_us_df[ca... | code |
33109004/cell_26 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
def plot_bar_vertical(df, figsize=(10, 15), xlabel='Count Number'):
ax = df.plot.barh(figsize=figsize)
for p in ax.patches:
ax.text(p.get_x() + p.get_width(), p.get_y() + p.get... | code |
33109004/cell_48 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
def plot_bar_vertical(df, figsize=(10, 15), xlabel='Count Number'):
ax = df.plot.barh(figsize=figsize)
for p in ax.patches:
ax.text(p.get_x() + p.get_width(), p.get_y() + p.get... | code |
33109004/cell_2 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
33109004/cell_54 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder,OneHotEncoder
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
def plot_bar_vertical(df, figsize=(10, 15), xlabel='Count Number'):
ax = df.plot.barh(figsize=figsize)
for p in ax.patches... | code |
33109004/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
car_us_df = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
sns.distplot(car_us_df.price, kde=False) | code |
33109004/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
def plot_bar_vertical(df, figsize=(10, 15), xlabel='Count Number'):
ax = df.plot.barh(figsize=figsize)
for p in ax.patches:
ax.text(p.get_x() + p.get_width(), p.get_y() + p.get... | code |
33109004/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
car_us_df = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
car_us_df.describe() | code |
33109004/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
def plot_bar_vertical(df, figsize=(10, 15), xlabel='Count Number'):
ax = df.plot.barh(figsize=figsize)
for p in ax.patches:
ax.text(p.get_x() + p.get_width(), p.get_y() + p.get... | code |
33109004/cell_51 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
def plot_bar_vertical(df, figsize=(10, 15), xlabel='Count Number'):
ax = df.plot.barh(figsize=figsize)
for p in ax.patches:
ax.text(p.get_x() + p.get_width(), p.get_y() + p.get... | code |
33109004/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
car_us_df = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
special_cases_price_zero = car_us_df[car_us_df.price == 0]
display(special_cases_price_zero.head(10))
special_cases_price_zero.shap... | code |
33109004/cell_46 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
def plot_bar_vertical(df, figsize=(10, 15), xlabel='Count Number'):
ax = df.plot.barh(figsize=figsize)
for p in ax.patches:
ax.text(p.get_x() + p.get_width(), p.get_y() + p.get... | code |
33109004/cell_22 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
def plot_bar_vertical(df, figsize=(10, 15), xlabel='Count Number'):
ax = df.plot.barh(figsize=figsize)
for p in ax.patches:
ax.text(p.get_x() + p.get_width(), p.get_y() + p.get... | code |
33109004/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
car_us_df = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
car_us_df.price.describe() | code |
33109004/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
car_us_df = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
car_us_df.head() | code |
33109004/cell_36 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
def plot_bar_vertical(df, figsize=(10, 15), xlabel='Count Number'):
ax = df.plot.barh(figsize=figsize)
for p in ax.patches:
ax.text(p.get_x() + p.get_width(), p.get_y() + p.get... | code |
104124059/cell_9 | [
"text_plain_output_1.png"
] | (1.0 - 4.85883614 / 97) * 100 | code |
104124059/cell_7 | [
"text_plain_output_1.png"
] | import scipy.sparse as sps
train_input = sps.load_npz('../input/open-problems-msci-multiome-sparse-matrices/train_multiome_input_sparse.npz')
def get_size(sparse_m):
size_gb = (sparse_m.indices.nbytes + sparse_m.indptr.nbytes + sparse_m.data.nbytes) * 1e-09
return f'Size: {size_gb} GB'
get_size(train_input) | code |
32069364/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.isnull().sum()
df2 = df
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['VAL... | code |
32069364/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.isnull().sum()
df['MORTDUE'].value_counts() | code |
32069364/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.isnull().sum() | code |
32069364/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df['REASON'].value_counts() | code |
32069364/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.isnull().sum()
df2 = df
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['VAL... | code |
32069364/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.isnull().sum()
df2 = df
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['VAL... | code |
32069364/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.isnull().sum()
df2 = df
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['VAL... | code |
32069364/cell_33 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.isnull().sum()
df2 = df
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
... | code |
32069364/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.isnull().sum()
df['MORTDUE'].min() | code |
32069364/cell_26 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.isnull().sum()
df2 = df
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['VAL... | code |
32069364/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.isnull().sum()
df.describe() | code |
32069364/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.isnull().sum()
df2 = df
df2 | code |
32069364/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 |
32069364/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.io as pio
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.isnull().sum()
df2 = df
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 ... | code |
32069364/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.select_dtypes('object').head() | code |
32069364/cell_15 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.isnull().sum()
plt.figure(figsize=(4, 4))
sns.countplot(y='REASON', data=df)
plt.figure(figsize=(8, 8))
sns.countplot(y='JOB', data=df... | code |
32069364/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.isnull().sum()
df[['LOAN', 'MORTDUE', 'VALUE', 'YOJ', 'CLAGE', 'CLNO', 'DEBTINC']].hist(figsize=[20, 20]) | code |
32069364/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('/kaggle/input/hmeq-data/hmeq.csv')
df.info() | code |
32069364/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.isnull().sum()
df2 = df
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['VAL... | code |
32069364/cell_24 | [
"image_output_5.png",
"image_output_4.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)
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.isnull().sum()
df2 = df
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['VAL... | code |
32069364/cell_27 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.isnull().sum()
df2 = df
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['VAL... | code |
32069364/cell_37 | [
"text_html_output_2.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.isnull().sum()
df2 = df
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DE... | code |
32069364/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.isnull().sum()
df['BAD'].value_counts().reset_index() | code |
32069364/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df['JOB'].value_counts() | code |
32069364/cell_36 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.isnull().sum()
df2 = df
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['VAL... | code |
32065554/cell_26 | [
"application_vnd.jupyter.stderr_output_1.png"
] | def function(stuff):
"""
Describe your function
"""
y = 0
return y
x = function(stuff)
x | code |
32065554/cell_15 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def get_number_of_elements_nested_list(list_of_keyword_lists):
counter = 0
for lst in list_of_keyword_lists:
counter += len(lst)
return counter
def evaluate_text_via_list_of_list_of_keywords(... | code |
32065554/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_dir = '../input/CORD-19-research-challenge/'
data_file = 'metadata.csv'
data = pd.read_csv(data_dir + data_file)
data.iloc[23643, 8] | code |
129029092/cell_21 | [
"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
df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
data = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
df.shape
data.shape
df.drop('PassengerId',... | code |
129029092/cell_9 | [
"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 seaborn as sns
df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
df.shape
sns.heatmap(df.isnull()) | code |
129029092/cell_30 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
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
df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
data ... | code |
129029092/cell_20 | [
"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
df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
data = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
df.shape
data.shape
df.drop('PassengerId',... | code |
129029092/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 = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
data = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data.head() | code |
129029092/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
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
df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
data ... | code |
129029092/cell_2 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns | code |
129029092/cell_11 | [
"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 seaborn as sns
df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
data = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
df.shape
data.shape
df.drop('PassengerId', axis=1)
df.drop('Name', axis=1, ... | code |
129029092/cell_19 | [
"text_plain_output_1.png",
"image_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
df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
data = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
df.shape
data.shape
df.drop('PassengerId',... | code |
129029092/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 |
129029092/cell_7 | [
"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 = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
df.shape | code |
129029092/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)
import seaborn as sns
df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
data = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
df.shape
data.shape
df.drop('PassengerId', axis=1)
df.drop('Name', axis=1, ... | code |
129029092/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import tree
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
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
df = pd.read_csv('/kaggle/input/spaceship-... | code |
129029092/cell_8 | [
"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 = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
data = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data.shape | code |
129029092/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)
import seaborn as sns
df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
data = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
df.shape
data.shape
df.drop('PassengerId', axis=1)
df.drop('Name', axis=1, ... | code |
129029092/cell_17 | [
"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 seaborn as sns
df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
data = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
df.shape
data.shape
df.drop('PassengerId', axis=1)
df.drop('Name', axis=1, ... | code |
129029092/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
data = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
df.shape
data.shape
df.drop('PassengerId', axis=1)
df.drop('Name', axis=1, ... | code |
129029092/cell_22 | [
"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
df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
data = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
df.shape
data.shape
df.drop('PassengerId',... | code |
129029092/cell_27 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_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)
import seaborn as sns
df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
data = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
df.shape
data.shape
df.drop('PassengerId', axis=1)
df.drop('Name', axis=1, ... | code |
129029092/cell_5 | [
"image_output_4.png",
"image_output_3.png",
"image_output_2.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 = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
df.head() | code |
106210118/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/marketing-strategy-personalised-offer/sample.csv')
df1 = pd.read_csv('../input/marketing-strategy-personalised-offer/train_data.csv')
feature_list = df1.columns[:-1].values
label = [df1.columns[-1... | code |
106210118/cell_2 | [
"text_html_output_1.png"
] | import os
import os
import numpy as np
import pandas as pd
import os
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
106210118/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/marketing-strategy-personalised-offer/sample.csv')
df1 = pd.read_csv('../input/marketing-strategy-personalised-offer/train_data.csv')
feature_list = df1.columns[:-1].values
label = [df1.columns[-1... | code |
106210118/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)
data = pd.read_csv('../input/marketing-strategy-personalised-offer/sample.csv')
df1 = pd.read_csv('../input/marketing-strategy-personalised-offer/train_data.csv')
df1.head(10) | code |
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