Pandas Introduction
Pandas is a powerful and open-source Python library. The Pandas library is used for data manipulation and analysis. Pandas consist of data structures and functions to perform efficient operations on data.
Pandas is well-suited for working with tabular data, such as spreadsheets or SQL tables.
To learn Pandas from basic to advanced, refer to our page: Pandas tutorial
What is Python Pandas used for?
The Pandas library is generally used for data science, but have you wondered why? This is because the Pandas library is used in conjunction with other libraries that are used for data science. It is built on top of the NumPy library which means that a lot of the structures of NumPy are used or replicated in Pandas.
The data produced by Pandas is often used as input for plotting functions in Matplotlib, statistical analysis in SciPy, and machine learning algorithms in Scikit-learn.
You must be wondering, Why should we use the Pandas Library. Python’s Pandas library is the best tool to analyze, clean, and manipulate data.
Here is a list of things that we can do using Pandas.
- Data set cleaning, merging, and joining.
- Easy handling of missing data (represented as NaN) in floating point as well as non-floating point data.
- Columns can be inserted and deleted from DataFrame and higher-dimensional objects.
- Powerful group by functionality for performing split-apply-combine operations on data sets.
- Data Visualization.
Getting Started with Pandas
Let’s see how to start working with the Python Pandas library:
Installing Pandas
The first step in working with Pandas is to ensure whether it is installed in the system or not. If not, then we need to install it on our system using the pip command.
For more reference, take a look at this article on installing pandas follows.
Importing Pandas
After the Pandas have been installed in the system, we need to import the library. This module is generally imported as follows:
import pandas as pd
Note: Here, pd is referred to as an alias for the Pandas. However, it is not necessary to import the library using the alias, it just helps in writing less code every time a method or property is called.
Data Structures in Pandas Library
Pandas generally provide two data structures for manipulating data. They are:
- Series
- DataFrame
Pandas Series
A Pandas Series is a one-dimensional labeled array capable of holding data of any type (integer, string, float, Python objects, etc.). The axis labels are collectively called indexes.
Creating a Series
Pandas Series is created by loading the datasets from existing storage (which can be a SQL database, a CSV file, or an Excel file).
Pandas Series can be created from lists, dictionaries, scalar values, etc.
Example: Creating a series using the Pandas Library.
import pandas as pd
import numpy as np
# Creating empty series
ser = pd.Series()
print("Pandas Series: ", ser)
# simple array
data = np.array(['g', 'e', 'e', 'k', 's'])
ser = pd.Series(data)
print("Pandas Series:\n", ser)
Output
Pandas Series: Series([], dtype: float64)
Pandas Series:
0 g
1 e
2 e
3 k
4 s
dtype: object
Pandas DataFrame
Pandas DataFrame is a two-dimensional data structure with labeled axes (rows and columns).
Creating DataFrame
Pandas DataFrame is created by loading the datasets from existing storage (which can be a SQL database, a CSV file, or an Excel file).
Pandas DataFrame can be created from lists, dictionaries, a list of dictionaries, etc.
Example: Creating a DataFrame Using the Pandas Library
import pandas as pd
# Calling DataFrame constructor
df = pd.DataFrame()
print(df)
# list of strings
lst = ['Geeks', 'For', 'Geeks', 'is', 'portal', 'for', 'Geeks']
# Calling DataFrame constructor on list
df = pd.DataFrame(lst)
print(df)
Output:
Empty DataFrame
Columns: []
Index: []
0
0 Geeks
1 For
2 Geeks
3 is
4 portal
5 for
6 Geeks
