How to create a DataFrame in Pandas

You can create a DataFrame in Pandas with the following code. I highly recommend you This book to learn Python. Pandas DataFrame is a 2-dimensional data structure, that looks like a table with rows and columns. You will see 12 examples of the Pandas DataFrame in this article.

  1. Empty DataFrame in Pandas
  2. DataFrame in Pandas from the list
  3. Create a DataFrame in Python with two lists
  4. Create a DataFrame from an array in Python
  5. Create a DataFrame in Python with Column Names
  6. Create a DataFrame in Python from the dictionary
  7. Create a DataFrame in Python with random values
  8. Pandas DataFrame from a list of dictionaries
  9. Pandas DataFrame from a list of tuples
  10. Pandas DataFrame from list of list
  11. DataFrame in Pandas from CSV
  12. Create a DataFrame in Python from Excel

Step 1: Install Pandas Library

Install the Pandas library using this code, if it is not installed.

pip install pandas

Empty DataFrame in Pandas

# Import the Pandas library as pd
import pandas as pd

# Create Empty DataFrame
a = pd.DataFrame()

# Display the DataFrame
print(a)

Output:

Empty DataFrame
Columns: []
Index: []

DataFrame in Pandas from the list

# Import the Pandas library as pd
import pandas as pd

# Initialize a list
a = [5, 12, 24, 55, 71]

# Create DataFrame
b = pd.DataFrame(a)

# Display the DataFrame
print(b)

Output:

    0
0   5
1  12
2  24
3  55
4  71

Create a DataFrame in Python with two lists

# Import the Pandas library as pd
import pandas as pd

# Initialize two lists
a = [5, 12, 24, 55, 71]
b = [21, 43, 13, 78, 97]

# Create DataFrame
c = pd.DataFrame(zip(a,b))

# Display the DataFrame
print(c)

Output:

    0   1
0   5  21
1  12  43
2  24  13
3  55  78
4  71  97

Create a DataFrame from an array in Python 

# Import the required libraries
import pandas as pd
import numpy as np

# Initialize a NumPy Array
a = np.array([[5, 10, 15, 40], [20, 25, 36, 55], [37, 48, 19, 70]])

# Create DataFrame
df = pd.DataFrame(a)

# Display the Output
print(df)

Output:

    0   1   2   3
0   5  10  15  40
1  20  25  36  55
2  37  48  19  70

Create a DataFrame in Python with Column Names

# Import the required libraries
import pandas as pd
import numpy as np

# Initialize a NumPy Array
a = np.array([[5, 10, 15, 75], [20, 25, 36, 85], [37, 48, 19, 17]])

# Create DataFrame
df = pd.DataFrame(a, columns=['A', 'B', 'C', 'D'])

# Display the Output
print(df)

Output:

    A   B   C   D
0   5  10  15  75
1  20  25  36  85
2  37  48  19  17

Create a DataFrame in Python from the dictionary

# Import the Pandas library as pd
import pandas as pd

# Initialize a dictionary
dict = {'Students':['John', 'Ahmad', 'Satish', 'Ali'],
        'Scores':[71, 70, 68, 73]}

# Create a DataFrame
df = pd.DataFrame(dict)

# Display the Output
print(df)

Output:

  Students  Scores
0     John      71
1    Ahmad      70
2   Satish      68
3      Ali      73

Create a DataFrame in Python with random values

# Import the required libraries
import pandas as pd
import numpy as np

# Create a Random Array of 5 rows and 3 columns 
# and values varies from 0 to 50
a = np.random.randint(0, 50, size=(5, 3))

# Create a DataFrame
df = pd.DataFrame(a, columns = ['A', 'B', 'C'])

# Display the Output
print(df)

Output:

    A   B   C
0  19  29   1
1  17  34  18
2  26  46  27
3   4  13  10
4  46   8   5

Pandas DataFrame from a list of dictionaries

# Import the Pandas library as pd
import pandas as pd
    
# Initialise list of dictionaries
a = [{'A': 'John', 'B': 'Ali', 'C': 'Harry'},
        {'A': 10, 'B': 20, 'C': 30},
       {'A': 2.5, 'B': 3.5, 'C':4.0, 'D':3.0 }] 
    
# Creates DataFrame. 
df = pd.DataFrame(a) 

# Display the Output
print(df)

Output:

      A    B      C    D
0  John  Ali  Harry  NaN
1    10   20     30  NaN
2   2.5  3.5    4.0  3.0

Create a Pandas DataFrame from a list of tuples 

# Import the Pandas library as pd
import pandas as pd

# Initialize a list of list
a = [(35, 12, 39, 72, 47),
     (15, 63, 82, 18, 9),
    (41, 92, 14, 84, 5)]

# Create DataFrame
df = pd.DataFrame(a, columns=['A','B','C','D','E'])

# Display the Output
print(df)

Output:

    A   B   C   D   E
0  35  12  39  72  47
1  15  63  82  18   9
2  41  92  14  84   5

Pandas DataFrame from list of list

# Import the Pandas library as pd
import pandas as pd

# Initialize a list of list
a = [[5, 10, 35, 12, 7],
     [15, 3, 2, 8, 9],
    [11, 32, 34, 54, 65]]

# Create DataFrame
df = pd.DataFrame(a, columns=['A','B','C','D','E'])

# Display the Output
print(df)

Output:

    A   B   C   D   E
0   5  10  35  12   7
1  15   3   2   8   9
2  11  32  34  54  65

Create a DataFrame in Pandas from CSV

# Import the Pandas library as pd
import pandas as pd

# Read CSV as Pandas DataFrame
a = pd.read_csv('house_price.csv')

# Display the first five rows of DataFrame
print(a.head())

Output:

   Area  Rooms  House_Age  Price
0  5000      5          5  75000
1  4000      4          5  65000
2  3000      3          1  60000
3  2000      3          1  58000
4  1500      2          1  50000

Create a DataFrame in Python from Excel

# Import the Pandas library as pd
import pandas as pd

# Read Excel as Pandas DataFrame
a = pd.read_excel('house_price.xlsx')

# Display the first five rows of DataFrame
print(a.head())

Output:

   Area  Rooms  House_Age  Price
0  5000      5          5  75000
1  4000      4          5  65000
2  3000      3          1  60000
3  2000      3          1  58000
4  1500      2          1  50000

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