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Certification - Data Scientist Associate - Electric Mopeds (copy)

Data Scientist Associate Practical Exam Submission

Use this template to complete your analysis and write up your summary for submission.

Importation of packages

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

Importation of dataset locally

bike_df = pd.read_csv('electric_bike_ratings_2212.csv')
print(bike_df.info())

Checking and resolving missing data

print(bike_df.isna().sum())
bike_df['web_browser'] = bike_df.web_browser.fillna('unknown')
bike_df.web_browser.value_counts() #to verify the filling of the missing values

Resolving data description of featues

#verifying some features
print(bike_df['owned'].value_counts())
print(bike_df['make_model'].value_counts())
print(bike_df['primary_use'].value_counts())

working on the reviewer_age, value_for_money and review_month features

bike_df.reviewer_age = bike_df.reviewer_age.str.replace('-', '0').astype('Float64')
bike_df.reviewer_age = bike_df.reviewer_age.replace(0, np.nan, inplace= False)
age_mean = bike_df.reviewer_age.mean()
bike_df.reviewer_age = bike_df.reviewer_age.fillna(age_mean, inplace = False)
bike_df.reviewer_age = bike_df.reviewer_age.astype('Int64')
bike_df
bike_df.value_for_money = bike_df.value_for_money.str.replace('/10', '')
bike_df.value_for_money = bike_df.value_for_money.astype('Int64')
bike_df
bike_df.review_month = bike_df.review_month.str.strip()
bike_df.review_month = bike_df.review_month.str.replace(r'..-', '', regex = True)
bike_df.review_month.value_counts()
bike_df.info()
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