path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
2032867/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
import warnings # We want to suppress warnings
warnings.filterwarnings('ignore')
HRData = pd.read_csv('../input/WA_Fn-UseC_-HR-Employee-Attrition.csv')
HRData.isnull().any()
hrdunique = HRData.nunique()
hrdunique = hrdunique.sort_values()
hrdunique
hrd = HRData.copy()
hrd.drop('Over18', axis... | code |
2032867/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import warnings # We want to suppress warnings
warnings.filterwarnings('ignore')
HRData = pd.read_csv('../input/WA_Fn-UseC_-HR-Employee-Attrition.csv')
HRData.isnull().any()
hrdunique = HRData.nunique()
hrdunique = hrdunique.sort_values()
hrdunique
hrd = HRData.copy()
... | code |
2032867/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import warnings # We want to suppress warnings
warnings.filterwarnings('ignore')
HRData = pd.read_csv('../input/WA_Fn-UseC_-HR-Employee-Attrition.csv')
HRData.isnull().any()
hrdunique = HRData.nunique()
hrdunique = hrdunique.sort_values()
hrdunique
hrd = HRData.copy()
... | code |
2032867/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import warnings # We want to suppress warnings
warnings.filterwarnings('ignore')
HRData = pd.read_csv('../input/WA_Fn-UseC_-HR-Employee-Attrition.csv')
HRData.isnull().any()
hrdunique = HRData.nunique()
hrdunique = hrdunique.sort_values()
hrdunique | code |
2032867/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import warnings # We want to suppress warnings
warnings.filterwarnings('ignore')
HRData = pd.read_csv('../input/WA_Fn-UseC_-HR-Employee-Attrition.csv')
HRData.isnull().any()
hrdunique = HRData.nunique()
hrdunique = hrdunique.sort_values()
hrdunique
hrd = HRData.copy()
... | code |
2032867/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import warnings # We want to suppress warnings
warnings.filterwarnings('ignore')
HRData = pd.read_csv('../input/WA_Fn-UseC_-HR-Employee-Attrition.csv')
HRData.isnull().any()
hrdunique = HRData.nunique()
hrdunique = hrdunique.sort_values()
hrdunique
hrd = HRData.copy()
hrd.drop('Over18', axis... | code |
2032867/cell_38 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib import colors
import numpy as np
import pandas as pd
import seaborn as sns
import warnings # We want to suppress warnings
warnings.filterwarnings('ignore')
HRData = pd.read_csv('../input/WA_Fn-UseC_-HR-Employee-Attrition.csv')
HRData.isnull().any()
hrdunique = HRData.nunique()
hrdunique = hrdu... | code |
2032867/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
import warnings # We want to suppress warnings
warnings.filterwarnings('ignore')
HRData = pd.read_csv('../input/WA_Fn-UseC_-HR-Employee-Attrition.csv')
HRData.isnull().any()
hrdunique = HRData.nunique()
hrdunique = hrdunique.sort_values()
hrdunique
hrd = HRData.copy()
hrd.drop('Over18', axis... | code |
2032867/cell_35 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib import colors
import numpy as np
import pandas as pd
import seaborn as sns
import warnings # We want to suppress warnings
warnings.filterwarnings('ignore')
HRData = pd.read_csv('../input/WA_Fn-UseC_-HR-Employee-Attrition.csv')
HRData.isnull().any()
hrdunique = HRData.nunique()
hrdunique = hrdu... | code |
2032867/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import warnings # We want to suppress warnings
warnings.filterwarnings('ignore')
HRData = pd.read_csv('../input/WA_Fn-UseC_-HR-Employee-Attrition.csv')
HRData.isnull().any()
hrdunique = HRData.nunique()
hrdunique = hrdunique.sort_values()
hrdunique
hrd = HRData.copy()
hrd.drop('Over18', axis... | code |
2032867/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import warnings # We want to suppress warnings
warnings.filterwarnings('ignore')
HRData = pd.read_csv('../input/WA_Fn-UseC_-HR-Employee-Attrition.csv')
HRData.info()
HRData.isnull().any() | code |
50233984/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/credit-card-customers/BankChurners.csv')
data = data.drop(columns=['Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1', 'Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_C... | code |
50233984/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/credit-card-customers/BankChurners.csv')
data.head(5) | code |
50233984/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/credit-card-customers/BankChurners.csv')
data = data.drop(columns=['Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1', 'Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_C... | code |
88094945/cell_63 | [
"text_plain_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
loan.groupby('Self_Employed')['Loan_Status'].value_counts()
loan.Credit_... | code |
88094945/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
sns.barplot(x='Gender', hue='Loan_Status', y='ApplicantIncome', data=loan) | code |
88094945/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan['Gender'].value_counts() | code |
88094945/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan['Married'].count() | code |
88094945/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.info() | code |
88094945/cell_57 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
loan.groupby('Self_Employed')['Loan_Status'].value... | code |
88094945/cell_34 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
sns.barplot(x='Married', hue='Loan_Status', y='Loa... | code |
88094945/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
sns.catplot(x='Gender', hue='Loan_Status', y='ApplicantIncome', data=loan, kind='bar', col='Education') | code |
88094945/cell_44 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
loan.groupby('Self_Employed')['Loan_Status'].value... | code |
88094945/cell_55 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
loan.groupby('Self_Employed')['Loan_Status'].value... | code |
88094945/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes | code |
88094945/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan['Married'].value_counts(normalize=True).plot.bar(title='Married') | code |
88094945/cell_39 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
loan['Self_Employed'].value_counts() | code |
88094945/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan['Married'].value_counts() | code |
88094945/cell_65 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
loan.groupby('Self_Employed')['Loan_Status'].value_counts()
loan.Credit_... | code |
88094945/cell_41 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
loan['Self_Employed'].value_counts(normalize=True).plot.bar(title='Self E... | code |
88094945/cell_61 | [
"text_plain_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
loan.groupby('Self_Employed')['Loan_Status'].value_counts()
loan.Credit_... | code |
88094945/cell_54 | [
"text_plain_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
loan.groupby('Self_Employed')['Loan_Status'].value_counts()
loan.Credit_... | code |
88094945/cell_60 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
loan.groupby('Self_Employed')['Loan_Status'].value_counts()
loan.Credit_... | code |
88094945/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
sns.countplot(x='Gender', hue='Loan_Status', data=loan) | code |
88094945/cell_50 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
loan.groupby('Self_Employed')['Loan_Status'].value_counts()
loan.Credit_... | code |
88094945/cell_52 | [
"text_plain_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
loan.groupby('Self_Employed')['Loan_Status'].value_counts()
loan.Credit_... | code |
88094945/cell_49 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
loan.groupby('Self_Employed')['Loan_Status'].value_counts()
loan['Credit... | code |
88094945/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts() | code |
88094945/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
sns.countplot(x='Married', hue='Loan_Status', data... | code |
88094945/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
sns.countplot(data=loan, x='Married') | code |
88094945/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan['Loan_Status'].value_counts() | code |
88094945/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
sns.countplot(data=loan, x='Gender') | code |
88094945/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan['Gender'].value_counts(normalize=True).plot.bar(title='Gender') | code |
88094945/cell_38 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
loan['Self_Employed'].count() | code |
88094945/cell_47 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
loan.groupby('Self_Employed')['Loan_Status'].value... | code |
88094945/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.head() | code |
88094945/cell_35 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
sns.barplot(x='Married', hue='Loan_Status', y='App... | code |
88094945/cell_43 | [
"text_plain_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
loan.groupby('Self_Employed')['Loan_Status'].value_counts() | code |
88094945/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts() | code |
88094945/cell_46 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
loan.groupby('Self_Employed')['Loan_Status'].value... | code |
88094945/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
sns.barplot(x='Gender', hue='Loan_Status', y='LoanAmount', data=loan) | code |
88094945/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
sns.countplot(data=loan, x='Loan_Status') | code |
88094945/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum() | code |
88094945/cell_36 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
sns.catplot(x='Married', hue='Loan_Status', y='App... | code |
32062089/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy import interpolate
df = pd.read_csv('../input/datasetauteur.csv')
df_maj = pd.read_csv('../input/MJ.csv', delimiter=';', encoding='utf-8')
df = df.append(df_maj, ignore_index=True, verify_integ... | code |
32062089/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy import interpolate
df = pd.read_csv('../input/datasetauteur.csv')
df_maj = pd.read_csv('../input/MJ.csv', delimiter=';', encoding='utf-8')
df = df.append(df_maj, ignore_index=True, verify_integ... | code |
32062089/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy import interpolate
df = pd.read_csv('../input/datasetauteur.csv')
df_maj = pd.read_csv('../input/MJ.csv', delimiter=';', encoding='utf-8')
df = df.append(df_maj, ignore_index=True, verify_integ... | code |
32062089/cell_40 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy import interpolate
df = pd.read_csv('../input/datasetauteur.csv')
df_maj = pd.read_csv('../input/MJ.csv', delimiter=';', encoding='utf-8'... | code |
32062089/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy import interpolate
df = pd.read_csv('../input/datasetauteur.csv')
df_maj = pd.read_csv('../input/MJ.csv', delimiter=';', encoding='utf-8')
df = df.append(df_maj, ignore_index=True, verify_integ... | code |
32062089/cell_45 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy import interpolate
df = pd.read_csv('../input/datasetauteur.csv')
df_maj = pd.read_csv('../input/MJ.csv', delimiter=';', encoding='utf-8'... | code |
32062089/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy import interpolate
df = pd.read_csv('../input/datasetauteur.csv')
df_maj = pd.read_csv('../input/MJ.csv', delimiter=';', encoding='utf-8')
df = df.append(df_maj, ignore_index=True, verify_integ... | code |
32062089/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy import interpolate
df = pd.read_csv('../input/datasetauteur.csv')
df_maj = pd.read_csv('../input/MJ.csv', delimiter=';', encoding='utf-8')
df = df.append(df_maj, ignore_index=True, verify_integ... | code |
32062089/cell_35 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy import interpolate
df = pd.read_csv('../input/datasetauteur.csv')
df_maj = pd.read_csv('../input/MJ.csv', delimiter=';', encoding='utf-8'... | code |
32062089/cell_43 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy import interpolate
df = pd.read_csv('../input/datasetauteur.csv')
df_maj = pd.read_csv('../input/MJ.csv', delimiter=';', encoding='utf-8')
df = df.append(df_maj, ignore_index=True, verify_integ... | code |
130008239/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
kw = pd.read_csv('/kaggle/input/2023-kaggle-ai-report/kaggle_writeups_20230510.csv', parse_dates=[0, 3])
kw['Title of Competition'].value_counts().tail(20) | code |
130008239/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
kw = pd.read_csv('/kaggle/input/2023-kaggle-ai-report/kaggle_writeups_20230510.csv', parse_dates=[0, 3])
kw.info() | code |
130008239/cell_2 | [
"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 |
130008239/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
kw = pd.read_csv('/kaggle/input/2023-kaggle-ai-report/kaggle_writeups_20230510.csv', parse_dates=[0, 3])
num_comp = kw['Title of Competition'].nunique()
print(f'The dataset has {num_comp} competitions.') | code |
130008239/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
kw = pd.read_csv('/kaggle/input/2023-kaggle-ai-report/kaggle_writeups_20230510.csv', parse_dates=[0, 3])
past_two_years_kw = kw[(kw['Competition Launch Date'].dt.year > 2020) & (kw['Competition Launch Date'].dt.month > 4)]
num_comp_past_two_years ... | code |
130008239/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
kw = pd.read_csv('/kaggle/input/2023-kaggle-ai-report/kaggle_writeups_20230510.csv', parse_dates=[0, 3])
earliest_comp = kw['Competition Launch Date'].min().strftime('%Y-%m-%d')
latest_comp = kw['Competition Launch Date'].max().strftime('%Y-%m-%d'... | code |
130008239/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
kw = pd.read_csv('/kaggle/input/2023-kaggle-ai-report/kaggle_writeups_20230510.csv', parse_dates=[0, 3])
kw['Title of Competition'].value_counts().head(20) | code |
130008239/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
kw = pd.read_csv('/kaggle/input/2023-kaggle-ai-report/kaggle_writeups_20230510.csv', parse_dates=[0, 3])
kw.head() | code |
33096880/cell_21 | [
"image_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
train.shape
corrMatrix = train.corr()
number = LabelEncoder()
train['status'] = number.fit_tran... | code |
33096880/cell_13 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
train.shape
corrMatrix = train.corr()
number = LabelEncoder()
train['status'] = number.fit_tran... | code |
33096880/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
train.shape | code |
33096880/cell_23 | [
"image_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
train.shape
corrMatrix = train.corr()
number = LabelEncoder()
train['status'] = number.fit_tran... | code |
33096880/cell_20 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
train.shape
corrMatrix = train.corr()
number = LabelEncoder()
train['status'] = number.fit_tran... | code |
33096880/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
train.shape
train.head() | code |
33096880/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
train.shape
corrMatrix = train.corr()
sns.heatmap(corrMatrix, annot=True)
plt.show() | code |
33096880/cell_19 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
train.shape
corrMatrix = train.corr()
number = LabelEncoder()
train['status'] = number.fit_tran... | code |
33096880/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
train.shape
train.tail() | code |
33096880/cell_18 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
train.shape
corrMatrix = train.corr()
number = LabelEncoder()
train['status'] = number.fit_tran... | code |
33096880/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
train.shape
train.info() | code |
33096880/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
train.shape
corrMatrix = train.corr()
number = LabelEncoder()
train['status'] = number.fit_tran... | code |
33096880/cell_16 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
train.shape
corrMatrix = train.corr()
number = LabelEncoder()
train['status'] = number.fit_tran... | code |
33096880/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
print(train.columns.values) | code |
33096880/cell_17 | [
"image_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
train.shape
corrMatrix = train.corr()
number = LabelEncoder()
train['status'] = number.fit_tran... | code |
33096880/cell_14 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
train.shape
corrMatrix = train.corr()
number = LabelEncoder()
train['status'] = number.fit_tran... | code |
33096880/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
train.shape
def find_missing_data(data):
Total = data.isnull().sum().sort_values(ascending=False)
Percentage = (data.isnull().sum() / data.isnull().count()).sort_values(ascending=False)
re... | code |
90109221/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
import numpy as np
import pandas as pd
mnist_train = pd.read_csv('../input/mnist-in-csv/mnist_train.csv')
mnist_test = pd.read_csv('../input/mnist-in-csv/mnist_test.csv')
X_train = mnist_train.iloc[:, 1:785]
y_train = mnist_train.iloc[:, 0]
lda = LDA(n_components=9)
X_trai... | code |
90109221/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
import numpy as np
import pandas as pd
mnist_train = pd.read_csv('../input/mnist-in-csv/mnist_train.csv')
mnist_test = pd.read_csv('../input/mnist-in-csv/mnist_test.csv')
X_train = mnist_train.iloc[:, 1:785]
y_train = mnist_train.iloc[:, 0]
lda = LDA(n_components=9)
X_trai... | code |
90109221/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import accuracy_score
import numpy as np
import pandas as pd
mnist_train = pd.read_csv('../input/mnist-in-csv/mnist_train.csv')
mnist_test = pd.read_csv('../input/mnist-in-csv/mnist_test.csv')
X_train = mnist_train.iloc[:, 1:785]
y_train = mnist_train.iloc[:, 0]
lda = LDA(n_components=9)
X_trai... | code |
90109221/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
import numpy as np
import pandas as pd
mnist_train = pd.read_csv('../input/mnist-in-csv/mnist_train.csv')
mnist_test = pd.read_csv('../input/mnist-in-csv/mnist_test.csv')
X_train = mnist_train.iloc[:, 1:785]
y_train = mnist_train.iloc[:, 0]
lda = LDA(n_components=9)
X_trai... | code |
90109221/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
import numpy as np
import pandas as pd
mnist_train = pd.read_csv('../input/mnist-in-csv/mnist_train.csv')
mnist_test = pd.read_csv('../input/mnist-in-csv/mnist_test.csv')
X_train = mnist_train.iloc[:, 1:785]
y_train = mnist_train.iloc[:, 0]
lda = LDA(n_components=9)
X_trai... | code |
90109221/cell_20 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
mnist_train = pd.read_csv('../input/mnist-in-csv/mnist_train.csv')
mnist_test = pd.read_csv('../input/mnist-in-csv/mnist_test.csv')
X_train = mnist_train.iloc[:, 1:785]
y_t... | code |
90109221/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import metrics
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
mnist_train = pd.read_csv('../input/mnist-in-csv/mnist_train.csv')
mnist_test = pd.read_csv('../input/mnist-in-csv/mnist_test.csv')
X_train = mnist_train.iloc[:, 1:785]
y_t... | code |
90109221/cell_11 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
mnist_train = pd.read_csv('../input/mnist-in-csv/mnist_train.csv')
mnist_test = pd.read_csv('../input/mnist-in-csv/mnist_test.csv')
X_train = mnist_train.iloc[:, 1:785]
y_t... | code |
90109221/cell_19 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
mnist_train = pd.read_csv('../input/mnist-in-csv/mnist_train.csv')
mnist_test = pd.read_csv('../input/mnist-in-csv/mnist_test.csv')
X_train = mnist_train.iloc[:, 1:785]
y_t... | code |
90109221/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
import pandas as pd
mnist_train = pd.read_csv('../input/mnist-in-csv/mnist_train.csv')
mnist_test = pd.read_csv('../input/mnist-in-csv/mnist_test.csv')
X_train = mnist_train.iloc[:, 1:785]
y_train = mnist_train.iloc[:, 0]
lda = LDA(n_components=9)
X_train_r2 = lda.fit(X_tra... | code |
90109221/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import accuracy_score
import numpy as np
import pandas as pd
mnist_train = pd.read_csv('../input/mnist-in-csv/mnist_train.csv')
mnist_test = pd.read_csv('../input/mnist-in-csv/mnist_test.csv')
X_train = mnist_train.iloc[:, 1:785]
y_train = mnist_train.iloc[:, 0]
lda = LDA(n_components=9)
X_trai... | code |
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