learn-co-students / dsc-pandas-series-and-dataframes-lab-chi01-dtsc-ft-051120

Understanding Pandas Series and DataFrames - Lab

Introduction

In this lab, let's get some hands-on practice working with data cleanup using Pandas.

Objectives

You will be able to:

• Use the .map() and .apply() methods to apply a function to a pandas Series or DataFrame
• Perform operations to change the structure of pandas DataFrames
• Change the index of a pandas DataFrame
• Change data types of columns in pandas DataFrames

Let's get started!

Import the file 'turnstile_180901.txt'.

# Import the required libraries
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline

# Import the file 'turnstile_180901.txt'
df = pd.read_csv('turnstile_180901.txt')

# Print the number of rows ans columns in df
print(df.shape)

# Print the first five rows of df
df.head()

Rename all the columns to lower case:

# Rename all the columns to lower case

Change the index to 'linename':

# Change the index to 'linename'

Reset the index:

# Reset the index

Create another column 'Num_Lines' that is a count of how many lines pass through a station. Then sort your DataFrame by this column in descending order.

Hint: According to the data dictionary, LINENAME represents all train lines that can be boarded at a given station. Normally lines are represented by one character. For example, LINENAME 456NQR represents trains 4, 5, 6, N, Q, and R.

# Add a new 'num_lines' column

Write a function to clean column names:

def clean(col_name):
    # Clean the column name in any way you want to. Hint: think back to str methods 
    cleaned = None
    return cleaned

# Use the above function to clean the column names

# Check to ensure the column names were cleaned
df.columns

• Change the data type of the 'date' column to a date
• Add a new column 'day_of_week' that represents the day of the week

# Convert the data type of the 'date' column to a date


# Add a new column 'day_of_week' that represents the day of the week 

# Group the data by day of week and plot the sum of the numeric columns
grouped = df.groupby('day_of_week').sum()
grouped.plot(kind='barh')
plt.show()

• Remove the index of grouped
• Print the first five rows of grouped

# Reset the index of grouped
grouped = None

# Print the first five rows of grouped

Add a new column 'is_weekend' that maps the 'day_of_week' column using the dictionary weekend_map

# Use this dictionary to create a new column 
weekend_map = {0:False, 1:False, 2:False, 3:False, 4:False, 5:True, 6:True}

# Add a new column 'is_weekend' that maps the 'day_of_week' column using weekend_map
grouped['is_weekend'] = grouped['day_of_week'].map(weekend_map)

# Group the data by weekend/weekday and plot the sum of the numeric columns
wkend = grouped.groupby('is_weekend').sum()
wkend[['entries', 'exits']].plot(kind='barh')
plt.show()

Remove the 'c/a' and 'scp' columns.

# Remove the 'c/a' and 'scp' columns
df = None
df.head(2)

Analysis Question

What is misleading about the day of week and weekend/weekday charts you just plotted?

# Your answer here 

Summary

Great! You practiced your data cleanup skills using Pandas.