Pandas is a very powerful data manipulation library based on the development logic in SQL. We are going to look at:

• Group By: split-apply-combine

• The Multi-Index

• Concatenate

• Merge and Join

• Reshaping and Pivot Tables

This lecture is part of the Data Science Fundamentals series.

• Previous: Exploratory Data Analysis with Pandas

• Next: Scrapping the Web for NLP applications

Group By: split-apply-combine

We use again the Titanic data as a toy dataset to demonstrate the different functionalities.

# We load the libraries
import pandas as pd
import seaborn as sns
import numpy as np

# we load the data
titanic_df = sns.load_dataset("titanic")
titanic_df

The groupby method works very much like in SQL. We can create a grouped object by columns

grouped = titanic_df.groupby(["survived", "sex"])

and we can apply methods on that object. For example we can sum by groups

# We sum by groups
grouped.sum()

Or we can count by groups

# we count by groups 
grouped.count()

We can look at the first row of each group

# we look at the first row
grouped.first()

We can look at the index of the different groups

grouped.groups

We can also iterate through the different groups

for name, group in grouped:
    print(name)
    print(group)

We can use the function agg (or aggregate) to apply function to the groups

# we sum per each group
grouped.agg(np.sum)

Which is equivalent to

grouped.sum()

We can apply many functions at once

# we compute the mean and the standard deviation
grouped.agg([np.mean, np.std])

We can use the transform method to transform the different groups

# we construct a function to normalize
normalize = lambda x: (x - x.mean()) / x.std() 

# and we apply it to each group
grouped[["pclass", "age", "sibsp"]].transform(normalize)

or equivalently

def normalize(x):
    return (x - x.mean()) / x.std()

grouped[["pclass", "age", "sibsp"]].transform(normalize)

The function apply can be used for more general use cases

# we can apply any function to each group
grouped[["pclass", "age", "sibsp"]].apply(lambda x: x.mean())

We can also plot by groups (which is kind of cool!)

grouped.plot()

The Multi-Index

The multi-index is one of most exciting perks of Pandas. It is often very useful to index the data with the existing columns. Here we create a multi-index