Alakhator Alakhator
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| df["User_Mean"] = df.groupby('User_ID')["Purchase"].transform('mean') |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| #importing libraries | |
| import pandas as pd | |
| import random |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| data = pd.DataFrame({ | |
| 'C' : [random.choice(('a','b','c')) for i in range(1000000)], | |
| 'A' : [random.randint(1,10) for i in range(1000000)], | |
| 'B' : [random.randint(1,10) for i in range(1000000)] | |
| }) |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| %%timeit | |
| data.groupby('C')["A"].mean() | |
| mean =data.groupby('C')["A"].mean().rename("N").reset_index() | |
| df_1 = data.merge(mean) |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| %%timeit | |
| data['N3'] = data.groupby(['C'])['A'].transform('mean') | |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| df['d'] = df.apply(lambda row: row.a + row.b + row.c, axis=1) |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| # Function to calculate VIF | |
| def calculate_vif(data): | |
| vif_df = pd.DataFrame(columns = ['Var', 'Vif']) | |
| x_var_names = data.columns | |
| for i in range(0, x_var_names.shape[0]): | |
| y = data[x_var_names[i]] | |
| x = data[x_var_names.drop([x_var_names[i]])] | |
| r_squared = sm.OLS(y,x).fit().rsquared | |
| vif = round(1/(1-r_squared),2) | |
| vif_df.loc[i] = [x_var_names[i], vif] |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| df = df.drop(df.columns[[0]], axis=1) | |
| calculate_vif(df) |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| #importing the libraries | |
| import pandas as pd | |
| import numpy as np | |
| #reading the dataset | |
| df=pd.read_csv("Salary.csv") |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| print df.info |