Python Pandas Data Manipulation

Data manipulation is a crucial step in the data analysis process. It involves transforming raw data into a format that can be used for analysis and visualization. In this article, we will discuss various techniques for manipulating data, including merging, joining, and concatenating. These techniques are used to combine multiple data sources into a single data set, making it easier to analyze and visualize the data.

Merging Data

Merging data is the process of combining two or more data sets into one. This is useful when you have data from multiple sources that you want to combine for analysis. For example, you may have sales data from two different regions that you want to merge into one data set to compare the sales from each region.

In Python, you can use the pandas library to perform data merging. The pandas library provides a function called merge that can be used to merge two data sets based on a common column. Let's take a look at an example:

import pandas as pd

# Create two data sets

data1 = {'Region': ['North', 'South', 'East', 'West'],

         'Sales': [10000, 12000, 11000, 9000]}

data2 = {'Region': ['North', 'South', 'East', 'West'],

         'Revenue': [20000, 22000, 21000, 19000]}

# Convert the data into data frames

df1 = pd.DataFrame(data1)

df2 = pd.DataFrame(data2)

# Merge the data frames based on the 'Region' column

merged_df = pd.merge(df1, df2, on='Region')

print(merged_df)

This code creates two data sets, data1 and data2, which contain sales data from different regions. The data sets are then converted into data frames df1 and df2. The pd.merge function is then used to merge the data frames based on the common column 'Region'. The resulting data frame, merged_df, contains both the sales and revenue data for each region.

Joining Data

Joining data is a similar process to merging data, but it uses a different method to combine the data. In a join, data is combined based on the values in the common columns of both data sets. There are three types of joins: inner join, outer join, and left join.

An inner join combines only the rows from both data sets that have matching values in the common columns. An outer join combines all the rows from both data sets, including the rows that do not have matching values in the common columns. A left join combines all the rows from the left data set and only the rows from the right data set that have matching values in the common columns.

Let's take a look at an example of an inner join:

  

import pandas as pd

# Create two data sets

data1 = {'Region': ['North', 'South', 'East', 'West'],

         'Sales': [10000, 12000, 11000, 9000]}

data2 = {'Region': ['North', 'South', 'East', 'West'],

         'Revenue': [20000, 22000, 21000, 19000]}

# Convert the data into data frames

df1 = pd.DataFrame(data1)

df2 = pd.DataFrame(data2)

# Perform an inner join on the 'Region' column

inner_join = df1.merge(df2, on='Region', how='inner')

print(inner_join)

This code creates two data sets, data1 and data2, which contain sales and revenue data from different regions. The data sets are then converted into data frames df1 and df2. The merge function is used to perform an inner join on the Region column. The resulting data frame, inner_join, contains only the rows where there is a matching value in the Region column in both df1 and df2.

In this example, the resulting data frame inner_join contains the same values as the merged data frame in the previous example, as an inner join will only retain the rows that have matching values in both data sets.

Amelioration

This article was researched and written with the help of ChatGPT, a language model developed by OpenAI.

Special thanks to ChatGPT for providing valuable information and examples used in this article.