In Pandas, the groupby functionality is a powerful tool for splitting a DataFrame into groups based on some criteria, applying a function to each group independently, and then combining the results into a new DataFrame. This process is often referred to as “split-apply-combine.”

Here’s an explanation of the key aspects of Pandas groupby objects:

Grouping Process:

  1. Splitting: The initial step involves breaking down the DataFrame into groups based on a specified column or columns. This is the “split” part of the process.
  2. Applying: After splitting, a function is applied independently to each group. This function can be any valid Pandas or user-defined function, and it operates on the data within each group.
  3. Combining: Finally, the results obtained from applying the function to each group are combined to create a new DataFrame, which often provides insights into the relationship between groups.

Properties of GroupBy Objects:

  1. GroupBy Object:
  • The result of the groupby operation is a GroupBy object. It’s a special DataFrame, essentially a collection of DataFrames, each corresponding to a group.
  • You can think of a GroupBy object as a mapping of group names (based on the grouping criteria) to the corresponding group’s DataFrame.

2. Accessing Groups:

  • Groups can be accessed using the get_group method, providing the name of the group. This allows for direct examination or manipulation of individual groups.

3. Iterating Over Groups:

  • The GroupBy object supports iteration, allowing you to loop through each group and perform operations on them individually.

4. Aggregation:

  • Common aggregation functions (e.g., sum, mean, count) can be applied to the GroupBy object to compute summary statistics for each group.

5. Transformation:

  • Transformation involves performing computations within each group but returning an object with the same shape as the original DataFrame. It’s done using the transform method.

6. Filtering:

  • Groups can be filtered based on certain conditions using the filter method, allowing you to include or exclude groups based on specific criteria.

7. Aggregation with agg:

  • The agg method allows for more complex aggregations, enabling you to specify different aggregation functions for different columns.

8. Applying Custom Functions:

  • You can apply custom functions to each group using the apply method. This flexibility is particularly useful for complex or specialized operations.

Example:

import pandas as pd

# Creating a sample DataFrame
data = {'Category': ['A', 'B', 'A', 'B', 'A', 'B'],
        'Value': [10, 15, 20, 25, 30, 35]}
df = pd.DataFrame(data)

# Grouping by 'Category'
grouped = df.groupby('Category')

# Applying the mean function to each group
mean_values = grouped.mean()
print(mean_values)

In this example, grouped is a GroupBy object, and calling mean() on it applies the mean function to each group, resulting in a DataFrame with the mean values for each category.

Understanding and effectively using the groupby functionality is crucial for analyzing and manipulating data efficiently in Pandas, especially when dealing with datasets that require grouping and aggregation operations.

Implementation Code

Datasets : pandas-GroupBy (kaggle.com)

1. Importing Libraries and Reading Data

import pandas as pd
import numpy as np

movies = pd.read_csv('/content/imdb-top-1000.csv')

Explanation: In this section, the necessary libraries (pandas and numpy) are imported. Then, the IMDb top 1000 movies dataset is read into a Pandas DataFrame named movies.

2. Grouping by Genre and Applying Aggregation

genres = movies.groupby('Genre')
genres.std()

Explanation: The movies DataFrame is grouped by the ‘Genre’ column. The standard deviation of each numerical column within each genre is computed and displayed.

3. Top Genres by Total Earnings

movies.groupby('Genre')['Gross'].sum().sort_values(ascending=False).head(3)

Explanation: This code finds the top 3 movie genres based on their total earnings (sum of ‘Gross’ values) and displays the results.

4. Genre with Highest Average IMDb Rating

movies.groupby('Genre')['IMDB_Rating'].mean().sort_values(ascending=False).head(1)

Explanation: It identifies the genre with the highest average IMDb rating and presents the result.

5. Director with Most Popularity

movies.groupby('Director')['No_of_Votes'].sum().sort_values(ascending=False).head(1)

Explanation: This code identifies the director with the most popularity, considering the sum of votes across all their movies.

6. Highest Rated Movie in Each Genre (Commented Out)

# movies.groupby('Genre')['IMDB_Rating'].max()

Explanation: This line is commented out, but if uncommented, it would show the highest IMDb rating for each genre.

7. Number of Movies by Each Actor

movies.groupby('Star1')['Series_Title'].count().sort_values(ascending=False)

Explanation: It counts the number of movies each actor (Star1) has appeared in and displays the result in descending order.

8. GroupBy Attributes and Methods (Commented Out)

# Various GroupBy attributes and methods are demonstrated, including len, size, first, last, nth, get_group, groups, describe, sample, and nunique.

Explanation: This section is commented out and provides examples of various GroupBy attributes and methods that can be applied.

9. Aggregation using agg method

genres.agg(
    {
        'Runtime':'mean',
        'IMDB_Rating':'mean',
        'No_of_Votes':'sum',
        'Gross':'sum',
        'Metascore':'min'
    }
)

Explanation: The agg method is used to perform multiple aggregations on different columns within each genre, calculating mean runtime, mean IMDb rating, total votes, total gross earnings, and minimum Metascore.

10. Looping on Groups

df = pd.DataFrame(columns=movies.columns)
for group, data in genres:
  df = df.append(data[data['IMDB_Rating'] == data['IMDB_Rating'].max()])
df

Explanation: It loops through each group (genre) and finds the movie with the maximum IMDb rating in each genre, creating a new DataFrame df with the results.

11. Split-Apply-Combine (Using apply with Builtin Function)

genres.apply(min)

Explanation: The apply function is used to apply the built-in min function to each group, finding the minimum value within each genre.

12. Custom Function using apply

def foo(group):
  return group['Series_Title'].str.startswith('A').sum()
genres.apply(foo)

Explanation: A custom function foo is applied to count the number of movies in each genre that start with the letter ‘A’.

13. Ranking Movies in Each Group

def rank_movie(group):
  group['genre_rank'] = group['IMDB_Rating'].rank(ascending=False)
  return group
genres.apply(rank_movie)

Explanation: A custom function rank_movie is applied to rank movies within each genre based on their IMDb rating, and the results are added as a new column (‘genre_rank’).

14. Normalizing IMDb Rating Group-Wise

def normal(group):
  group['norm_rating'] = (group['IMDB_Rating'] - group['IMDB_Rating'].min()) / (group['IMDB_Rating'].max() - group['IMDB_Rating'].min())
  return group
genres.apply(normal)

Explanation: A custom function normal is applied to normalize IMDb ratings within each genre, creating a new column (‘norm_rating’).

15. Grouping on Multiple Columns

duo = movies.groupby(['Director','Star1'])
duo
duo.size()
duo.get_group(('Aamir Khan','Amole Gupte'))
duo['Gross'].sum().sort_values(ascending=False).head(1)

Explanation: The DataFrame is grouped by both ‘Director’ and ‘Star1’. Various operations are performed, including getting the size, retrieving a specific group, and finding the most earning actor->director combo.

16. Best Actor-Genre Combo by Metascore

movies.groupby(['Star1','Genre'])['Metascore'].mean().reset_index().sort_values('Metascore',ascending=False).head(1)

Explanation: It finds the best actor->genre combo based on the average Metascore, sorting by the highest Metascore.

17. Aggregation on Multiple GroupBy

duo.agg(['min','max','mean'])

Explanation: Multiple aggregations are performed on the grouped DataFrame duo for ‘min’, ‘max’, and ‘mean’ values.

18. IPL Dataset Exploration

ipl = pd.read_csv('/content/deliveries.csv')
ipl.head()

Explanation: The IPL deliveries dataset is read into a DataFrame named ipl, and the first few rows are displayed.

19. Top 10 Batsmen by Runs

ipl.groupby('batsman')['batsman_runs'].sum().sort_values(ascending=False).head(10)

Explanation: It identifies the top 10 batsmen in terms of total runs scored in the IPL.

20. Batsman with Most Sixes

six = ipl[ipl['batsman_runs'] == 6]
six.groupby('batsman')['batsman'].count().sort_values(ascending=False).head(1).index[0]

Explanation: It finds the batsman with the maximum number of sixes in the IPL.

21. Batsman with Most 4’s and 6’s in Last 5 Overs

temp_df = ipl[ipl['over'] > 15]
temp_df = temp_df[(temp_df['batsman_runs'] == 4) | (temp_df['batsman_runs'] == 6

)]
temp_df.groupby('batsman')['batsman'].count().sort_values(ascending=False).head(1).index[0]

Explanation: It identifies the batsman with the most number of 4’s and 6’s in the last 5 overs of IPL matches.

22. Virat Kohli’s Record Against All Teams

temp_df = ipl[ipl['batsman'] == 'V Kohli']
temp_df.groupby('bowling_team')['batsman_runs'].sum().reset_index()

Explanation: It shows Virat Kohli’s batting performance against each bowling team in the IPL.

23. Function to Get Highest Score of Any Batsman

def highest(batsman):
  temp_df = ipl[ipl['batsman'] == batsman]
  return temp_df.groupby('match_id')['batsman_runs'].sum().sort_values(ascending=False).head(1).values[0]

highest('DA Warner')

Explanation: A function highest is defined to get the highest score of any batsman in a single IPL match. An example with David Warner is provided.

These explanations provide an overview of each code section’s purpose and functionality.

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