Pandas: Create Dataframe from List of Dictionaries

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Methods of creating a dataframe from a list of dictionaries

In this article, we discuss different methods by which we can create a dataframe from a list of dictionaries. Before going to the actual article let us done some observations that help to understand the concept easily. Suppose we have a list of dictionary:-

list_of_dict = [
{'Name': 'Mayank' , 'Age': 25, 'Marks': 91},
{'Name': 'Raj', 'Age': 21, 'Marks': 97},
{'Name': 'Rahul', 'Age': 23, 'Marks': 79},
{'Name': 'Manish' , 'Age': 23},
]

Here we know that dictionaries consist of key-value pairs. So we can analyze that if we make the key as our column name and values as the column value then a dataframe is easily created. And we have a list of dictionaries so a dataframe with multiple rows also.

pandas.DataFrame

This methods helps us to create dataframe in python

syntax: pandas.DataFrame(data=None, index=None, columns=None, dtype=None, copy=False)

Let us see different methods to create dataframe from a list of dictionaries

  • Method 1-Create Dataframe from list of dictionaries with default indexes

As we see in in pandas.Datframe() method there is parameter name data.We have to simply pass our list of dictionaries in this method and it will return the dataframe.Let see this with the help of an example.

import pandas as pd
import numpy as np

list_of_dict = [
    {'Name': 'Mayank' ,  'Age': 25,  'Marks': 91},
    {'Name': 'Raj',  'Age': 21,  'Marks': 97},
    {'Name': 'Rahul',  'Age': 23,  'Marks': 79},
    {'Name': 'Manish' ,  'Age': 23,  'Marks': 86},
]
#create dataframe
df=pd.DataFrame(list_of_dict)
print(df)

Output

   Age  Marks    Name
0   25     91  Mayank
1   21     97     Raj
2   23     79   Rahul
3   23     86  Manish

Here we see that dataframe is created with default indexes 0,1,2,3….

Now a question may arise if from any dictionary key-value pair is less than other dictionaries.So in this case what happened.Let understand it with the help of an example.

import pandas as pd
import numpy as np

list_of_dict = [
    {'Name': 'Mayank' ,  'Age': 25,  'Marks': 91},
    {'Name': 'Raj',  'Age': 21,  'Marks': 97},
    {'Name': 'Rahul',  'Marks': 79},
    {'Name': 'Manish' ,  'Age': 23},
]
#create dataframe
df=pd.DataFrame(list_of_dict)
print(df)

Output

    Age  Marks    Name
0  25.0   91.0  Mayank
1  21.0   97.0     Raj
2   NaN   79.0   Rahul
3  23.0    NaN  Manish

Here we see in case of missing key value pair NaN value is there in the output.

  • Method 2- Create Dataframe from list of dictionary with custom indexes

Unlike the previous method where we have default indexes we can also give custom indexes by passes list of indexes in index parameter of pandas.DataFrame() function.Let see this with the help of an example.

import pandas as pd
import numpy as np

list_of_dict = [
    {'Name': 'Mayank' ,  'Age': 25,  'Marks': 91},
    {'Name': 'Raj',  'Age': 21,  'Marks': 97},
    {'Name': 'Rahul',  'Marks': 79},
    {'Name': 'Manish' ,  'Age': 23},
]
#create dataframe
df=pd.DataFrame(list_of_dict,index=['a','b','c','d'])
print(df)

Output

    Age  Marks    Name
a  25.0   91.0  Mayank
b  21.0   97.0     Raj
c   NaN   79.0   Rahul
d  23.0    NaN  Manish

Here we see that instead of default index 1,2,3….. we have now indes a,b,c,d.

  • Method 3-Create Dataframe from list of dictionaries with changed order of columns

With the help of pandas.DataFrame() method we can easily arrange order of column by simply passes list ozf columns in columns parameter in the order in which we want to display it in our dataframe.Let see this with the help of example.

import pandas as pd
import numpy as np

list_of_dict = [
    {'Name': 'Mayank' ,  'Age': 25,  'Marks': 91},
    {'Name': 'Raj',  'Age': 21,  'Marks': 97},
    {'Name': 'Rahul',  'Age': 23,  'Marks': 79},
    {'Name': 'Manish' ,  'Age': 23,  'Marks': 86},
]
#create dataframe
df=pd.DataFrame(list_of_dict,columns=['Name', 'Marks', 'Age'])
print(df)

Output

     Name  Marks  Age
0  Mayank     91   25
1     Raj     97   21
2   Rahul     79   23
3  Manish     86   23

Here also a question may arise if we pass less column in columns parameter or we pass more column in parameter then what happened.Let see this with the help of an example.

Case 1: Less column in column parameter

In this case the column which we don’t pass will be drop from the dataframe.Let see this with the help of an example.

import pandas as pd
import numpy as np

list_of_dict = [
    {'Name': 'Mayank' ,  'Age': 25,  'Marks': 91},
    {'Name': 'Raj',  'Age': 21,  'Marks': 97},
    {'Name': 'Rahul',  'Age': 23,  'Marks': 79},
    {'Name': 'Manish' ,  'Age': 23,  'Marks': 86},
]
#create dataframe
df=pd.DataFrame(list_of_dict,columns=['Name', 'Marks'])
print(df)

Output

     Name  Marks
0  Mayank     91
1     Raj     97
2   Rahul     79
3  Manish     86

Here we see that we didn’t pass Age column that’s why Age clumn is also not in our dataframe.

Case 2: More column in column parameter

In this case a new column will be added in dataframe but its all the value will be NaN.Let see this with the help of an example.

import pandas as pd
import numpy as np

list_of_dict = [
    {'Name': 'Mayank' ,  'Age': 25,  'Marks': 91},
    {'Name': 'Raj',  'Age': 21,  'Marks': 97},
    {'Name': 'Rahul',  'Age': 23,  'Marks': 79},
    {'Name': 'Manish' ,  'Age': 23,  'Marks': 86},
]
#create dataframe
df=pd.DataFrame(list_of_dict,columns=['Name', 'Marks', 'Age','city'])
print(df)

Output

     Name  Marks  Age  city
0  Mayank     91   25   NaN
1     Raj     97   21   NaN
2   Rahul     79   23   NaN
3  Manish     86   23   NaN

So these are the methods to create dataframe from list of dictionary in pandas.