How to Plot Duplicate Columns In Python Pandas?

6 minutes read

To plot duplicate columns in Python using the Pandas library, you can follow these steps:


First, import the necessary libraries:

1
2
import pandas as pd
import matplotlib.pyplot as plt


Next, read in your dataset using pd.read_csv() or any other relevant function:

1
data = pd.read_csv('your_dataset.csv')


Identify the duplicate columns in your dataset using the duplicated() function:

1
duplicate_columns = data.columns[data.columns.duplicated()]


Plot the duplicate columns using the plot() function:

1
2
data[duplicate_columns].plot()
plt.show()


This will plot the duplicate columns from your dataset using matplotlib. You can customize the plot by adding labels, titles, adjusting the plot size, etc.


Remember to replace 'your_dataset.csv' with your actual dataset filename or path.


Note: Duplicate columns refer to multiple columns in a DataFrame that have the same column name. This assumes that the duplicate columns contain numeric or continuous data that can be plotted.

Best Python Books of January 2024

1
Learning Python, 5th Edition

Rating is 5 out of 5

Learning Python, 5th Edition

2
Head First Python: A Brain-Friendly Guide

Rating is 4.9 out of 5

Head First Python: A Brain-Friendly Guide

3
Python for Beginners: 2 Books in 1: Python Programming for Beginners, Python Workbook

Rating is 4.8 out of 5

Python for Beginners: 2 Books in 1: Python Programming for Beginners, Python Workbook

4
Python All-in-One For Dummies (For Dummies (Computer/Tech))

Rating is 4.7 out of 5

Python All-in-One For Dummies (For Dummies (Computer/Tech))

5
Python for Everybody: Exploring Data in Python 3

Rating is 4.6 out of 5

Python for Everybody: Exploring Data in Python 3

6
Learn Python Programming: The no-nonsense, beginner's guide to programming, data science, and web development with Python 3.7, 2nd Edition

Rating is 4.5 out of 5

Learn Python Programming: The no-nonsense, beginner's guide to programming, data science, and web development with Python 3.7, 2nd Edition

7
Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition

Rating is 4.4 out of 5

Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition


What is the value_counts() function in Pandas?

The value_counts() function in Pandas is a method that can be applied to a Pandas Series to count the frequency of unique values in the Series. It returns a new Series object with the unique values as the index and the count of each unique value as the corresponding value. The resulting Series is sorted in descending order by default.


What is the mean() function in Pandas?

The mean() function in Pandas is used to calculate the mean (average) of a series or column in a DataFrame. It calculates the arithmetic average of all the values in the specified series or column.


What is the dtypes property in Pandas?

The dtypes property in pandas is used to identify the data type of each column in a pandas DataFrame. It returns a pandas Series object where the column names are the index and the data type of each column is the corresponding value.


For example, consider a pandas DataFrame df:

1
2
3
4
   Column1  Column2  Column3
0        1      1.5     True
1        2      2.5    False
2        3      3.5     True


To get the data types of each column, you can use the dtypes property:

1
2
3
4
5
>>> df.dtypes
Column1       int64
Column2     float64
Column3    bool
dtype: object


From the output, you can see that the data type of "Column1" is int64, "Column2" is float64, and "Column3" is a boolean (bool).

Facebook Twitter LinkedIn Telegram Whatsapp Pocket

Related Posts:

To plot data from a Pandas DataFrame with Matplotlib, you can follow these steps:Import the required libraries: import pandas as pd import matplotlib.pyplot as plt Load or create a Pandas DataFrame with data that you want to plot. Decide on the type of plot yo...
To turn a list of lists into columns in a Pandas dataframe, you can use the DataFrame() constructor provided by the Pandas library. Here's the process:Import the Pandas library: import pandas as pd Define the list of lists that you want to convert into col...
To reverse a Pandas series, you can make use of the slicing technique with a step value of -1. Follow these steps:Import the Pandas library: import pandas as pd Create a Pandas series: data = [1, 2, 3, 4, 5] series = pd.Series(data) Reverse the series using sl...