Delve into effective data manipulation with pandas. Discover filtering, merging, grouping, and reshaping techniques through hands-on examples, best practices, and performance tips for real-world analysis.

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The course is a comprehensive guide to using pandas for data analysis. It covers a wide range of topics related to data manipulation, including filtering, merging, grouping, pivoting, and reshaping data.

In this course, you’ll learn about the best practices and efficient techniques for working with data. You’ll be provided with numerous examples of real-world data analysis problems and gain hands-on experience with the pandas library to solve them effectively. This course will also cover performance considerations and provide insights on common pitfalls to avoid when using pandas.

The course is suitable for both beginners and experienced users of pandas. It includes detailed explanations of pandas concepts and functions, as well as tips and tricks for optimizing your code. By the time you’re done with this course, you’ll be able to effectively handle data using pandas.

Effective Data Manipulation with pandas

100 56 17
12  23 1
49  3  65
32 19  41
99 100 41

Create a DataFrame named `df`. Loop over each row and calculate the maximum and minimum values.

import numpy as np
import pandas as pd
 
matrix = [(100, 56, 17),
   (12, 23, 1),
   (49, 3, 65),
   (32, 19, 43),
   (99, 100, 41)
   ]
# Create a DataFrame
df = pd.DataFrame(matrix, index = list('abcde'), columns = list('xyz'))

# iterate through each row and select
 
for i in range(len(df)):
   print("max value in row", i,": ",max(df.iloc[i, 0], df.iloc[i,1], df.iloc[i, 2]))
   print("min value in row", i,": ",min(df.iloc[i, 0], df.iloc[i,1], df.iloc[i, 2]))

See the solution to the problems related to looping, aggregation, and the apply() method.

Introduction

Series Deep Dive

DataFrames

Manipulating Data

Wrapping Up

Appendix

Solution: Looping, Aggregation, and apply() Method

Problem 1: Solution