FUNCTIONS IN PANDAS

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In this tutorial, we will learn about different types of functions in Pandas that will help us to understand and use pandas more efficiently for solving different types of tasks.

functions-in-pandas

Understanding Functions in Pandas

By now we know how to create different types of data structures in Pandas. We have learned about creating a Series and a DataFrame. Now it’s time to learn about different functionalities in Pandas to perform different tasks.

A few functionalities are:

Functions Description
dtypes It returns the type of data 
empty Checks whether the Dataframe is empty or not. If yes, then it turns True. 
ndim Returns the number of dimensions of the dataframe.
size Returns the size of the data structure
head() Returns rows of the data that you specify inside the parentheses from the beginning.
tail() Returns rows of the data that you specify inside the parentheses from the last..
Transpose  Converts rows into columns and columns into rows

We will be looking at these functions examples one by one to understand more about them:

Functions in Pandas: dtypes

It returns the type of data

df.dtypes

Output:

Persons object
Jobs object
Dtype: object



Functions in Pandas: empty

Checks whether the Dataframe is empty or not. If yes, then it turns True.

df.empty

Output:

False

Since our dataframe is not empty hence empty returned False.

Functions in Pandas: ndim

Returns the number of dimensions of the dataframe.

df.ndim

Output:
2

Functions in Pandas: size

Returns the size of the data structure (number of rows and columns):

df.size

Output:

8

head()

Returns rows of the data that you specify inside the parentheses from the beginning.

df.head(2)

Output:

            Persons         Jobs
First       Hira            Entrepreneur
Second      Sanjeev         Doctor

tail()

Returns rows of the data that you specify inside the parentheses

df.tail(1)

Output:

         Persons   Jobs
Fourth   Ali       Chef

axes

axes function returns the rows axis lable and column axis label. Let’s look at a quick example:

import pandas as pd # intialise data of lists. 
data = {'Name':['Hira', 'Sanjeev', 'Rahul', 'Ali'], 
'Occupation':['Entrepreneur', 'Doctor', 'Actor', 'Chef'], 
'Salary':[30000, 40000, 25000, 32000], 'Age':[25,24,27,29]} 
# Create DataFrame 
df = pd.DataFrame(data) 
# Print the output. 
print(df)
df.axes

Output:

      Name    Occupation  Salary  Age
0     Hira  Entrepreneur   30000   25
1  Sanjeev        Doctor   40000   24
2    Rahul         Actor   25000   27
3      Ali          Chef   32000   29

[RangeIndex(start=0, stop=4, step=1),
 Index(['Name', 'Occupation', 'Salary', 'Age'], dtype='object')]

Above, you can see that we are able to create axis labels of rows and columns by simply using the axes function. It is displaying the range index as well as a separated index from the dictionary keys.

Transpose

Converts rows into columns and columns into rows

df.T

Output:

           First          Second    Third   Fourth
Persons    Hira           Sanjeev   Rahul   Ali
Jobs       Entrepreneur   Doctor    Actor   Chef

Rows are converted into columns

To learn more about other general functions present in pandas, check out the latest documentation here



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