How to do DATA Wrangling in a Pandas DataFrame in Python

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How to do DATA Wrangling in a Pandas DataFrame in Python

Data Wrangling is the process of cleaning, transforming, and organizing data in a way that makes it suitable for analysis and visualization. It is an essential step in the data analysis process and is often used to prepare data for machine learning or other forms of analysis. In this blog post, we will discuss how to do data wrangling in a Pandas DataFrame in Python.

Pandas is a powerful Python library that is commonly used for data analysis and manipulation. One of its main features is the DataFrame, which is a table-like data structure that can be used to organize and manipulate data.

There are several steps involved in data wrangling a Pandas DataFrame:

Loading Data: The first step is to load the data into a Pandas DataFrame. This can be done using the read_csv() function, which reads data from a CSV file and stores it in a DataFrame. Other file formats can also be loaded using their corresponding functions like read_json(), read_html() etc.

Exploring Data: Once the data is loaded, it’s important to explore the data and get a sense of its structure and content. This can be done using functions like head(), tail(), info(), and describe(). These functions can be used to view the first or last few rows of the DataFrame, get information about the data types and number of non-null values in each column, and generate summary statistics of the data.

Cleaning Data: After exploring the data, the next step is to clean it by removing or correcting any errors or missing values. This can be done using functions like dropna(), fillna(), and drop_duplicates(). These functions can be used to remove rows or columns that have missing or null values, fill in missing values with a specific value or method, and remove duplicate rows from the DataFrame.

Transforming Data: Once the data is cleaned, the next step is to transform it so that it’s in a format that can be easily analyzed. This can be done using functions like groupby(), pivot_table(), and melt(). These functions can be used to group data by a specific column, create a pivot table to summarize data, and reshape the data to make it easier to work with.

Organizing Data: Finally, it’s important to organize the data so that it can be easily accessed and analyzed. This can be done using functions like sort_values() and set_index(). These functions can be used to sort the DataFrame by a specific column and set the index to a specific column.

In conclusion, data wrangling is an important step in the data analysis process, and Pandas provides several functions and methods to make it easy to clean, transform, and organize data in a Python script. It helps you to transform the raw data into a format that is suitable for analysis, thus making the entire process more efficient and accurate.


In this Learn through Codes example, you will learn: How to do DATA Wrangling in a Pandas DataFrame in Python.

Code Explanation

The code you provided is a Python script that demonstrates how to use the Pandas library to do data wrangling on a DataFrame and a Series. The Pandas library is a powerful tool for data analysis and manipulation in Python.

The script starts by importing the Pandas library and turning off warning messages.

First, it demonstrates how to work with a Pandas Series, which is a one-dimensional array-like data structure. The script creates a series called ‘floodingReports’ using the pd.Series() function and assigns the values [5, 6, 2, 9, 12].

Next, it sets county names as the index of the series using the index parameter. It then prints the series and demonstrates how to access the value of a specific element in the series using its index. After that it filters the series by using a comparison operator on the series, so that it prints only those elements which are greater than 6.

Then it creates a dictionary called ‘fireReports_dict’ with county names as keys and number of fire reports as values, then it converts that dictionary into a pd.Series, and changes its index to shorter names.

After that, it demonstrates how to work with a Pandas DataFrame. A DataFrame is a two-dimensional table-like data structure that can hold multiple data types and can be used to store and manipulate data. The script creates a DataFrame ‘df’ from a dictionary containing equal-length lists. Then it prints the DataFrame.

Then it demonstrates how to set the order of columns using the columns attribute. It creates a new DataFrame ‘dfColumnOrdered’ using the same data, but with the column order specified in the columns parameter. After that, it adds a new column to the DataFrame and then deletes that column.

Finally, it demonstrates how to transpose the DataFrame using the T attribute. This will switch the rows and columns of the DataFrame.

In conclusion, the script demonstrates how to use the Pandas library to do data wrangling on a DataFrame and a Series. The script uses several functions and attributes of the Pandas library to load, clean, and transform data, making it more suitable for analysis. It shows how to use Pandas Dataframe and Series to load, clean and manipulate data efficiently, which is very useful in data science, analysis and machine learning projects.


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