How to Make Pandas Dataframe From List Of Dictionaries?

10 minutes read

To create a pandas dataframe from a list of dictionaries, you can simply use the pd.DataFrame() function and pass the list of dictionaries as an argument. Each dictionary in the list will become a row in the dataframe, with the keys of the dictionaries becoming the column names. This can be a quick and efficient way to convert structured data into a dataframe for further analysis and manipulation in Python using the pandas library.

Best Python Books to Read In October 2024

1
Learning Python, 5th Edition

Rating is 5 out of 5

Learning Python, 5th Edition

  • O'Reilly Media
2
Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and The Cloud

Rating is 4.9 out of 5

Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and The Cloud

3
Python Crash Course, 2nd Edition: A Hands-On, Project-Based Introduction to Programming

Rating is 4.8 out of 5

Python Crash Course, 2nd Edition: A Hands-On, Project-Based Introduction to Programming

4
Learn Python 3 the Hard Way: A Very Simple Introduction to the Terrifyingly Beautiful World of Computers and Code (Zed Shaw's Hard Way Series)

Rating is 4.7 out of 5

Learn Python 3 the Hard Way: A Very Simple Introduction to the Terrifyingly Beautiful World of Computers and Code (Zed Shaw's Hard Way Series)

5
Python for Beginners: 2 Books in 1: Python Programming for Beginners, Python Workbook

Rating is 4.6 out of 5

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

6
The Python Workshop: Learn to code in Python and kickstart your career in software development or data science

Rating is 4.5 out of 5

The Python Workshop: Learn to code in Python and kickstart your career in software development or data science

7
Introducing Python: Modern Computing in Simple Packages

Rating is 4.4 out of 5

Introducing Python: Modern Computing in Simple Packages

8
Head First Python: A Brain-Friendly Guide

Rating is 4.3 out of 5

Head First Python: A Brain-Friendly Guide

  • O\'Reilly Media
9
Python All-in-One For Dummies (For Dummies (Computer/Tech))

Rating is 4.2 out of 5

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

10
The Quick Python Book

Rating is 4.1 out of 5

The Quick Python Book

11
Python Programming: An Introduction to Computer Science, 3rd Ed.

Rating is 4 out of 5

Python Programming: An Introduction to Computer Science, 3rd Ed.

12
Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition

Rating is 3.9 out of 5

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


What is the purpose of the reset_index method in pandas dataframes?

The purpose of the reset_index method in pandas dataframes is to reset the index of the dataframe. This method can be useful when the original index needs to be reset or if the index needs to be converted to a column in the dataframe. By using this method, the current index of the dataframe is reset to the default integer index starting from 0, and a new default index column is added to the dataframe. This can be helpful for data manipulation and analysis, as it allows for easier slicing, merging, and reshaping of the dataframe.


How to perform arithmetic operations on columns in a pandas dataframe?

To perform arithmetic operations on columns in a pandas dataframe, you can use the following steps:

  1. Create a pandas dataframe:
1
2
3
4
5
6
import pandas as pd

data = {'A': [1, 2, 3, 4],
        'B': [5, 6, 7, 8]}
df = pd.DataFrame(data)
print(df)


  1. Perform arithmetic operations on columns:


You can perform arithmetic operations such as addition, subtraction, multiplication, and division on columns using the following syntax:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
# Addition
df['C'] = df['A'] + df['B']

# Subtraction
df['D'] = df['A'] - df['B']

# Multiplication
df['E'] = df['A'] * df['B']

# Division
df['F'] = df['B'] / df['A']

print(df)


This will add new columns 'C', 'D', 'E', and 'F' to the dataframe with the results of the arithmetic operations performed on columns 'A' and 'B'.

  1. Use the result for further analysis or visualization.


You can now use the result of the arithmetic operations for further analysis, visualization, or any other data processing tasks in your pandas dataframe.


How to rename columns in a pandas dataframe?

You can rename columns in a pandas dataframe using the rename method. Here's an example:

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

# Create a sample dataframe
data = {'A': [1, 2, 3], 'B': [4, 5, 6]}
df = pd.DataFrame(data)

# Rename columns
df = df.rename(columns={'A': 'X', 'B': 'Y'})

print(df)


This will output:

1
2
3
4
   X  Y
0  1  4
1  2  5
2  3  6


In this example, we used the rename method to rename columns 'A' to 'X' and 'B' to 'Y' in the dataframe. The columns parameter is a dictionary where keys are the old column names and values are the new column names.

Twitter LinkedIn Telegram Whatsapp

Related Posts:

To convert a JSON object or file to a DataFrame in Pandas, you can use the pd.read_json() method. This function will read the JSON data and convert it into a DataFrame format. You can pass the JSON object directly as a parameter or provide the path to the JSON...
To delete a specific column from a pandas dataframe, you can use the drop() method along with the axis parameter set to 1. For example, if you want to delete a column named "column_name" from a dataframe called df, you can do so by using df.drop('c...
To sort a pandas dataframe by month name, you can convert the 'datetime' column to a 'CategoricalDtype' with the categories listed as month names. Then, you can use the 'sort_values' function with the 'CategoricalDtype' to sort ...