How To Slicing, Adding Columns in DataFrame | Pandas Tutorial 3 | Big Data Tutorials | Codeing School

This is Pandas Tutorial Part 3. pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.

Slicing, Adding Columns in DataFrame | Pandas Tutorial 3

Today’s Topic: Find How Many Rows and Columns in DataFrame, Slicing in DataFrame, and Adding Columns in DataFrame.

First import pandas and numpy:

import pandas as pd

import numpy as np

Then, take a DataFrame:

df1 = pd.DataFrame([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16],[17,18,19,20]])

df1

Output:

	0	1	2	3
0	1	2	3	4
1	5	6	7	8
2	9	10	11	12
3	13	14	15	16
4	17	18	19	20

Img. no. 2. Take a DataFrame

Find How Many Rows and Columns in DataFrame:

df1.shape

Output:

(5, 4)

Logic: With the help of this df1.shape syntax, we can find out how many rows and columns in our DataFrame. (5, 4), it means we have 5, Rows and 4, Columns.

Find how many Roe=ws and Columns you have in a DataFrame

Slicing in DataFrame:

Find element in DataFrame:

I want to find ‘10’, at our DataFrame (See, Image 2.).

df1.iloc(2,1)

Output:

10

Logic: We want ‘10’ in the DataFrame. Therefore, I use this syntax df.iloc (2,1). It means (2 is Row and 1 is Column) 3^rd Row’s 2^nd Column’s value.

Find element in dataFrame

Access the matrix [10, 11, 14, 15], in DataFrame:

These Values,

df1.iloc(2:4, 1:3)

Output:

	1	2
2	10	11
3	14	15

Logic: We use this df1.iloc(2:4, 1:3) syntax for slicing a matrix in a DataFrame. In this code (2:4, 1:3) means, We start the Row, 2^nd number of row to 4^th number of Row, because, Slicing logic says, “Starting index to end index + 1”. Here starting index is 2 and end index is 3 + 1.

In the same manner, I take the columns. “1:3” means 1^st index to 2^nd index + 1. That’s why I get the output, in a matrix format.

Access Matrix

Adding Columns in DataFrame:

We can add the columns name in Pandas.

df2 = pd.DataFrame([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16],[17,18,19,20]], columns=['A','B','C','D'])

df2

Output:

	A	B	C	D
0	1	2	3	4
1	5	6	7	8
2	9	10	11	12
3	13	14	15	16
4	17	18	19	20

Logic: With the help of this syntax, we can add the column in DataFrame. Here, we have 4 columns, so, “columns = ['A','B','C','D']”.

Adding Column 

If you have, any quarry please comment below.