The Best Python Pandas Tutorial

Python pandas is one of the most widely-used Python libraries in data science and analytics. It provides high-performance, easy-to-use structures, and data analysis tools. Two-dimensional table objects in pandas are referred to as DataFrame, as well as Series. It is a structure that contains column names and row labels. 

In this article, we'll be discussing the following topics:

  • What are Python pandas?
  • Pandas Series
  • Basic Operations on Series
  • Pandas DataFrames
  • Basic Operations on DataFrames
  • Python Pandas: Sorting
  • Python Pandas: Merging
  • Python Pandas: GroupBy
  • Python Pandas: Concatenation

What is Python Pandas?

Pandas is an open-source Python library that provides high-performance, easy-to-use data structure, and data analysis tools for the Python programming language

Python with pandas is used in a wide range of fields, including academics, retail, finance, economics, statistics, analytics, and many others.

Python pandas is well suited for different kinds of data, such as:

  • Ordered and unordered time series data
  • Unlabeled data
  • Any other form of observational or statistical data sets

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Pandas Series

Series is a one-dimensional array that can contain any type of data. You can create a series by using the following constructor:

pandas.Series(data, index, dtype, copy)

Example:

import-panda.

Fig: importing pandas module

Basic Operations on Series

  • Create a series from ndarray

ndarray

Fig: ndarray series

If you don’t mention the index of the array, it begins at zero by default.

  • Create a series from a dictionary

A dictionary data structure can be passed as an input in the series.

Example:

series

Fig: Series from a dictionary

  • Accessing data from a series

To access the data in the series, we enter the index number of the element or the label on an element.

Example:

access

Fig: Access data in a series

To retrieve data using labels, we enter the label value.

Example:

retrieving

Fig: Retrieving data by label name

Pandas DataFrame

A DataFrame is a multi-dimensional data structure in which data is arranged in the form of rows and columns. You can create a DataFrame using the following constructor:

pandas.DataFrame(data, index, columns, dtype, copy)

Example:

empty.

Fig: Empty DataFrame

Basic Operations on DataFrames

  • Create a DataFrame from lists

A DataFrame can be created using a list:

dataframe1

Fig: DataFrame

dataframe2

Fig: 2-D DataFrame

  • Creating a DataFrame from a series dictionary

A series dictionary can be passed to form a DataFrame.

Example:

dataframe-series

Fig: DataFrame from a Series dictionary

Let us now look at the column selection, addition, and deletion, and indexing a DataFrame through an example.

  • Column selection

You select a particular column by mentioning the column name.

Example:

/column.

Fig: Column selection

  • Addition of a new column

The following enables users to incorporate new columns into the data provided: 

adding.

Fig: Adding a new column

  • Deleting a column

Columns can be deleted using the del or pop functions.

Example:

del.

Fig: Deleting a column

  • Indexing a DataFrame

The iloc() method is used for integer-based indexing. 

Example:

/iloc

Fig: iloc()

Python Pandas Sorting

There are two types of sorting available in pandas. They are:

  • By label
  • By actual value

  • By Label

The sort_index() method is used to sort data in pandas. You pass the axis arguments and order of the sorting.

Example:

/sorting

Fig: Sorting by label

By default, sorting is done in ascending order.

  • By Actual Value

The sort_values() method is used to sort the column according to values.

Example:

by

Fig: By actual value

Python Pandas GroupBy

The groupby function performs one of the following operations on original data. They include:

  • Splitting the object
  • Applying a function
  • Combining the result

Let’s create a DataFrame object and perform all the operations.

Example:

dataframe3.

Fig: DataFrame

Split Data by Groups

Let us see how grouping objects can be used in DataFrames.

Example:

splitting-data

Fig: Splitting data into groups

View Groups

view-groups

Fig: View groups

Python Pandas: Merging

You can merge two DataFrames by including the key in the following way:

merging.

Fig: Merging two DataFrames

In the above program, we used the ‘id’ column as a common key.

Python Pandas: Concatenation

The concat function is used to concatenate two DataFrames.

Example:

concatenation.

Fig: Concatenation

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Conclusion

In this Python pandas tutorial, we covered Python pandas and its different functions. We also provided a visual example that demonstrated how to use DataFrames and Series in Python pandas. 

If you have any questions or comments, please post them below, and we'll have our experts get back to you as soon as possible.

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