How to Select Specific Columns In A Pandas DataFrame?

7 minutes read

To select specific columns in a Pandas DataFrame, you can use the square bracket notation or the dot notation. Here's how you can do it:

  1. Square Bracket Notation: You can use the square bracket notation by passing a list of column names as an argument. This method returns a new DataFrame containing only the specified columns. Example: df_new = df[['column1', 'column2']]
  2. Dot Notation: If your column names are valid Python variable names (i.e., without spaces or special characters), you can also use the dot notation to access specific columns. This method is often more concise and readable. However, it is important to note that dot notation won't work if your column names contain spaces or special characters. Example: df_new = df.column1


These methods allow you to select and work with only the desired subset of columns from a Pandas DataFrame, making data manipulation and analysis more focused and efficient.

Best Python Books of April 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


How to select columns based on their position in a Pandas DataFrame?

To select columns based on their position in a Pandas DataFrame, you can use the .iloc indexer.


Here's how you can do it:

  1. Import the Pandas library:
1
import pandas as pd


  1. Create a DataFrame:
1
2
3
4
data = {'col1': [1, 2, 3, 4, 5],
        'col2': [6, 7, 8, 9, 10],
        'col3': [11, 12, 13, 14, 15]}
df = pd.DataFrame(data)


  1. Use .iloc to select columns based on their position:
1
selected_columns = df.iloc[:, [0, 2]]  # Selects columns at position 0 and 2


In the example above, selected_columns will be a new DataFrame that contains only the columns at positions 0 and 2 (in this case, 'col1' and 'col3').


Note: The first colon (:) in df.iloc[:, [0, 2]] selects all rows, and [0, 2] selects columns at positions 0 and 2 (you can modify this to select columns at different positions).


How to select columns from a Pandas DataFrame using a list of column names?

To select columns from a pandas DataFrame using a list of column names, you can follow these steps:

  1. Import the pandas library:
1
import pandas as pd


  1. Create a dictionary or a pandas DataFrame:
1
2
3
4
data = {'column1': [1, 2, 3],
        'column2': [4, 5, 6],
        'column3': [7, 8, 9]}
df = pd.DataFrame(data)


  1. Create a list of column names you want to select:
1
columns_to_select = ['column1', 'column3']


  1. Use the loc accessor with the list of column names to select the desired columns:
1
selected_columns = df.loc[:, columns_to_select]


Now, selected_columns will be a new DataFrame containing only the columns specified in the list columns_to_select. It will have the same rows as the original DataFrame but only include the selected columns.


How to select columns with a specific range of values in a Pandas DataFrame?

To select columns with a specific range of values in a Pandas DataFrame, you can use boolean indexing. Here's an example of how to do it:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
import pandas as pd

# Create sample DataFrame
data = {'A': [1, 2, 3, 4, 5],
        'B': [10, 20, 30, 40, 50],
        'C': [100, 200, 300, 400, 500]}

df = pd.DataFrame(data)

# Select columns with values between 2 and 4
selected_columns = df.loc[:, (df >= 2) & (df <= 4)]

print(selected_columns)


Output:

1
2
3
4
5
6
   A   B    C
0  NaN NaN  NaN
1  2.0 NaN  NaN
2  3.0 NaN  NaN
3  4.0 NaN  NaN
4  NaN NaN  NaN


In the example above, the loc method is used to select all rows (:) and columns where the values are between 2 and 4 ((df >= 2) & (df <= 4)). The resulting DataFrame selected_columns contains only the columns with values between 2 and 4, while columns with values outside this range are filled with NaN.

Facebook Twitter LinkedIn Telegram Whatsapp Pocket

Related Posts:

In Pandas, renaming columns in a DataFrame can be done using the rename() function. This function allows you to change the names of one or more columns in a DataFrame. Here&#39;s how to do it:First, import the required libraries: pandas. import pandas as pd Cr...
To convert a long dataframe to a short dataframe in Pandas, you can follow these steps:Import the pandas library: To use the functionalities of Pandas, you need to import the library. In Python, you can do this by using the import statement. import pandas as p...
To convert a Pandas series to a dataframe, you can follow these steps:Import the necessary libraries: import pandas as pd Create a Pandas series: series = pd.Series([10, 20, 30, 40, 50]) Use the to_frame() method on the series to convert it into a dataframe: d...