Python | Creating DataFrame from dict of narray/lists

As we know Pandas is all-time great tools for data analysis. One of the most important data type is dataframe. It is a 2-dimensional labeled data structure with columns of potentially different types. It is generally the most commonly used pandas object. 
Pandas DataFrame can be created in multiple ways. Let’s discuss how to create Pandas dataframe using dictionary of ndarray (or lists). Let’s try to understand it better with a few examples.

Using Pandas DataFrame to create Dataframe from List

Example #1:  We will use pd.DataFrame() function to create the dataframe from the list.

Python3

# Python code demonstrate creating
# DataFrame from dict narray / lists
# By default addresses.

import pandas as pd

# initialise data of lists.
data = {'Category':['Array', 'Stack', 'Queue'],
        'Marks':[20, 21, 19]}

# Create DataFrame
df = pd.DataFrame(data)

# Print the output.
print(df)

Output:

  Category  Marks
0    Array     20
1    Stack     21
2    Queue     19

Note: To create DataFrame from dict of narray/list, all the narray must be of same length. If index is passed then the length index should be equal to the length of arrays. If no index is passed, then by default, index will be range(n) where n is the array length.

Example #2:

Python3

# Python code demonstrate creating
# DataFrame from dict narray / lists
# By default addresses.

import pandas as pd

# initialise data of lists.
data = {'Category':['Array', 'Stack', 'Queue'],
        'Student_1':[20, 21, 19], 'Student_2':[15, 20, 14]}

# Create DataFrame
df = pd.DataFrame(data)

# Print the output.
print(df.transpose())

Output:

               0      1      2
Category   Array  Stack  Queue
Student_1     20     21     19
Student_2     15     20     14

Using Pandas Dataframe with the index parameter

Example #1: Providing index list to dataframe 

Python3

# Python code demonstrate creating
# DataFrame from dict narray / lists
# By default addresses.

import pandas as pd

# initialise data of lists.
data = {'Area':['Array', 'Stack', 'Queue'],
        'Student_1':[20, 21, 19], 'Student_2':[15, 20, 14]}

# Create DataFrame
df = pd.DataFrame(data, index =['Cat_1', 'Cat_2', 'Cat_3'])

# Print the output.
print(df)

Output:

        Area  Student_1  Student_2
Cat_1  Array         20         15
Cat_2  Stack         21         20
Cat_3  Queue         19         14