KevsRobots Learning Platform

Data Manipulation with Pandas and Numpy

64% Percent Complete

Overview
Introduction to Pandas
Pandas
MatPlotLib
Conclusion

Data Analysis and Aggregation

Explore the powerful data analysis and aggregation capabilities of Pandas, including summarizing data, grouping, and performing aggregate computations.

By Kevin McAleer,    2 Minutes

Cover photo

Overview

This lesson delves into Data Analysis and Aggregation with Pandas. Effective data analysis often involves summarizing data, grouping it based on certain criteria, and performing aggregate computations. We will explore these powerful capabilities in Pandas to derive meaningful insights from data.

Summarizing Data

Descriptive Statistics

Pandas provides convenient methods to get descriptive statistics:

# Descriptive statistics
summary = df.describe()

The describe() method returns a Data Frame with descriptive statistics for each column in the original Data Frame, like this:

    Column1    Column2
count  5.000000  5.000000
mean   0.000000  0.000000
std    0.707107  0.707107
min   -1.000000 -1.000000
25%   -1.000000 -1.000000
50%    0.000000  0.000000
75%    1.000000  1.000000
max    1.000000  1.000000

Unique Values, Value Counts, and Membership

You can also explore unique values and counts:

# Unique values and counts
unique_values = df['ColumnName'].unique()
value_counts = df['ColumnName'].value_counts()

Grouping Data

Group By

Group data by one or more columns:

# Grouping data
grouped = df.groupby('ColumnName')

Aggregating Data

Perform aggregate computations on groups:

# Aggregating data
aggregated_data = grouped.aggregate(np.sum)

Pivot Tables

Create pivot tables for multidimensional data analysis:

# Pivot tables
pivot_table = df.pivot_table(values='ValueColumn', index='RowColumn', columns='ColumnColumn')

Summary

In this lesson, we’ve explored essential aspects of data analysis and aggregation using Pandas. Understanding how to summarize, group, and aggregate data is crucial for effective data analysis and gaining insights.

< Previous Next >