Grouping in pandas [1/2]

To perform grouping in pandas, apply the .groupby() method to dataframe, with the column that will determine the groups passed as the parameter.

Note that grouping itself doesn't change the dataframe. Grouping always should come in pair with some aggregation.

For example, our dataframe has the 'morgh' column, representing if the dwelling is mortgaged or not (1 - Yes, 2 - No, 0 - House is being rented). Let's count number of household within each of possible groups (by applying the .size() method).


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# Importing the library
import pandas as pd

# Reading the file
df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/f2947b09-5f0d-4ad9-992f-ec0b87cd4b3f/data5.csv')

# Grouping and aggregating data
print(df.groupby('morgh').size())

As you can see, 248 people are currently renting a house, while among 771 owners 365 have mortgages.

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Section 3. Chapter 2

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Course Content

Data Manipulation using pandas

1. Preprocessing Data: Part I

What is Data Preprocessing?Types consistency Poor Data Presentation Manipulating Strings Challenge Replacing Specific Elements Simultaneous Replacement Challenge

2. Preprocessing Data: Part II

Logical Inconsistency Removing Rows Challenge Outliers Challenge Missing Values Filling NA values Challenge

3. Grouping Data

What is Grouping Data?Grouping in pandas [1/2]Challenge Grouping in pandas [2/2]Challenge Grouping by Several Columns Challenge

4. Aggregating and Visualizing Data

Advanced Aggregation [1/2]Challenge Advanced Aggregation [2/2]Challenge Histograms Challenge Bar and Scatter Plots Other Types of Graphs Challenge 1 Challenge 2

5. Joining Data

What is Joining Data?Left Join Right Join Inner Join Outer Join Concatenation