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Data Analyst Professional Practical Exam Submission

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1. Data Validation

The dataset contains 1500 rows and 8 columns before cleaning and validataion. I have validated all the columns against the criteria in the dataset table:

  • booking_id: unique identifier of the booking without missing values, same as the description. No cleaning is needed.
  • months_as_member: The number of months as this fitness club member, minimum 1 month, same as the description. No cleaning is needed.
  • weight: the member's weight in kg, same as the description. 20 missing values were replace by overall average.
  • days_before: numeric values. No missing values. Dtype converted to int.
  • day_of_week: day of the week of the class. Fix the wrong week day names and changed dtype to category. No missing values.
  • time: time of the day of the class, same as the description. No missing values. Change dtype to category.
  • category: category of the fitness class. 13 missing values replace to "unknown". Dtype changed to category.
  • attended: same as the description. No cleaning is needed.

After the data validation, the dataset contains 1500 rows and 8 columns without missing values.

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

sns.set_palette('colorblind')
sns.set_theme()
pd.set_option('display.expand_frame_repr', False)
df = pd.read_csv('fitness_class_2212.csv')
df.head()
df.info()
df.describe()
Hidden output 
df.isna().sum()
Hidden output 
mean_weight = df['weight'].mean()
df['weight'].fillna(value=mean_weight, inplace=True)

df.isna().sum()
Hidden output 
df['days_before'] = df['days_before'].str.strip(' days')
df['days_before'] = df['days_before'].astype(int)
df.info()
df['day_of_week'] = df['day_of_week'].str.replace('Fri.', 'Fri')
df['day_of_week'] = df['day_of_week'].str.replace('Monday', 'Mon')
df['day_of_week'] = df['day_of_week'].str.replace('Wednesday', 'Wed')

df['day_of_week'] = df['day_of_week'].astype('category')
1 hidden cell
df['time'] = df['time'].astype('category')
df['category'] = df['category'].str.replace('-', 'Unknown')
df['category'] = df['category'].astype('category')
df.info()
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