EDS 217 Cheatsheet

Grouping, Aggregating, and Pivot Tables in Pandas

Grouping Data

Grouping data allows you to split your DataFrame into groups based on one or more columns.

Code 
import pandas as pd

# Sample DataFrame
df = pd.DataFrame({
    'category': ['A', 'B', 'A', 'B', 'A'],
    'value': [1, 2, 3, 4, 5]
})
print(df)
  category  value
0        A      1
1        B      2
2        A      3
3        B      4
4        A      5

Creating a groupby object:

Code 
# Group by 'category'
grouped = df.groupby('category')

Aggregating Data

After grouping, you can apply various aggregation functions to summarize the data within each group.

Basic aggregation

Code 
# Basic aggregations
print(grouped['value'].mean())
print(grouped['value'].sum())
category
A    3.0
B    3.0
Name: value, dtype: float64
category
A    9
B    6
Name: value, dtype: int64

Doing multiple aggregations at the same time using agg()

Code 
# Multiple aggregations
print(grouped['value'].agg(['mean', 'sum', 'count']))
          mean  sum  count
category                  
A          3.0    9      3
B          3.0    6      2

Aggregation using a custom function

Code 
# Custom aggregation function
def range_func(x):
    return x.max() - x.min()

print(grouped['value'].agg(range_func))
category
A    4
B    2
Name: value, dtype: int64

Common Aggregation Functions

  • mean(): Average
  • sum(): Sum of values
  • count(): Count of non-null values
  • min(), max(): Minimum and maximum values
  • median(): Median value
  • std(), var(): Standard deviation and variance
  • first(), last(): First and last values in the group

Grouped Operations

You can apply operations to each group separately using transform() or apply().

Using transform() to alter each group in a group by object

Code 
# Transform: apply function to each group, return same-sized DataFrame
def normalize(x):
    return (x - x.mean()) / x.std()

df['value_normalized'] = grouped['value'].transform(normalize)

Using apply() to alter each group in a group by object

Code 
# Apply: apply function to each group, return a DataFrame or Series
def group_range(x):
    return x['value'].max() - x['value'].min()

result = grouped.apply(group_range, include_groups=False)

Pivot Tables

Pivot tables are a powerful tool for reorganizing and summarizing data. They allow you to transform your data from a long format to a wide format, making it easier to analyze and visualize patterns.

Working with Pivot Tables

Code 
# Sample DataFrame
df = pd.DataFrame({
    'date': ['2023-01-01', '2023-01-01', '2023-01-02', '2023-01-02'],
    'product': ['A', 'B', 'A', 'B'],
    'sales': [100, 150, 120, 180]
})
print(df)
         date product  sales
0  2023-01-01       A    100
1  2023-01-01       B    150
2  2023-01-02       A    120
3  2023-01-02       B    180

Pivot tables with a single aggregation function

Code 
# Create a pivot table
pivot_table = pd.pivot_table(df, values='sales', index='date', columns='product', aggfunc='sum')
print(pivot_table)
product       A    B
date                
2023-01-01  100  150
2023-01-02  120  180

Pivot tables with multiple aggregation

Code 
# Pivot table with multiple aggregation functions
pivot_multi = pd.pivot_table(df, values='sales', index='date', columns='product', 
                             aggfunc=['sum', 'mean'])
print(pivot_multi)
            sum        mean       
product       A    B      A      B
date                              
2023-01-01  100  150  100.0  150.0
2023-01-02  120  180  120.0  180.0

Key Pivot Table Parameters

  • values: Column(s) to aggregate
  • index: Column(s) to use as row labels
  • columns: Column(s) to use as column labels
  • aggfunc: Function(s) to use for aggregation (default is mean)
  • fill_value: Value to use for missing data
  • margins: Add row/column with subtotals (default is False)

For more detailed information on grouping, aggregating, and pivot tables in Pandas, refer to the official Pandas documentation.