Pandas is a popular library for data manipulation and analysis in Python. It provides various data structures such as Series, Dataframe, and Panel. In this article, we will discuss how to convert a dictionary to a Pandas Dataframe.
Using the from_dict() Method
The easiest way to convert a dictionary to a Pandas Dataframe is by using the from_dict() method. This method creates a Dataframe from a dictionary.
import pandas as pd
my_dict = {'name': ['John', 'Mary', 'Peter', 'Jane'],
'age': [25, 30, 35, 40],
'country': ['USA', 'Canada', 'UK', 'Australia']}
df = pd.DataFrame.from_dict(my_dict)
print(df)
[Output]
name age country 0 John 25 USA 1 Mary 30 Canada 2 Peter 35 UK 3 Jane 40 Australia
As you can see, the from_dict() method creates a Dataframe from the dictionary. The keys of the dictionary become the column names, and the values become the values in the Dataframe.
Using the DataFrame() Constructor
We can also use the DataFrame() constructor to create a Pandas Dataframe from a dictionary. This method allows us to specify the column names and index labels of the Dataframe.
import pandas as pd
my_dict = {'name': ['John', 'Mary', 'Peter', 'Jane'],
'age': [25, 30, 35, 40],
'country': ['USA', 'Canada', 'UK', 'Australia']}
df = pd.DataFrame(data=my_dict, columns=['name', 'age', 'country'],
index=['a', 'b', 'c', 'd'])
print(df)
[Output]
name age country a John 25 USA b Mary 30 Canada c Peter 35 UK d Jane 40 Australia
As you can see, we have specified the column names and index labels in the DataFrame() constructor. This method allows us to customize the Dataframe according to our requirements.
Using the pd.json_normalize() method
pd.json_normalize() method is a powerful method that can be used to normalize semi-structured JSON data into a flat table. However, it can also be used to convert a dictionary into a pandas dataframe.
import pandas as pd
# Creating dictionary
dictionary = {'Name': ['John', 'Smith', 'Anna', 'Jake'],
'Age': [34, 28, 29, 42],
'Gender': ['M', 'M', 'F', 'M']}
# Converting dictionary to pandas dataframe
df = pd.json_normalize(dictionary)
# Displaying dataframe
print(df)
Output:
Name Age Gender 0 John 34 M 1 Smith 28 M 2 Anna 29 F 3 Jake 42 M
Using the pd.DataFrame() constructor and Transposing
import pandas as pd
# Creating dictionary
dictionary = {'Name': ['John', 'Smith', 'Anna', 'Jake'],
'Age': [34, 28, 29, 42],
'Gender': ['M', 'M', 'F', 'M']}
# Converting dictionary to pandas dataframe
df = pd.DataFrame([dictionary])
# Transposing the dataframe
df = df.T.reset_index()
# Renaming the columns
df.columns = ['Feature', 'Value']
# Displaying dataframe
print(df)
Output:
Feature Value 0 Name John 1 Age 34 2 Gender M 3 Name Smith 4 Age 28 5 Gender M 6 Name Anna 7 Age 29 8 Gender F 9 Name Jake 10 Age 42 11 Gender M
Last Updated on May 16, 2023 by admin