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

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:

Fig: importing pandas module

Basic Operations on Series

• Create a series from 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:

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:

Fig: Access data in a series

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

Example:

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:

Fig: Empty DataFrame

Basic Operations on DataFrames

• Create a DataFrame from lists

A DataFrame can be created using a list:

Fig: DataFrame

Fig: 2-D DataFrame

• Creating a DataFrame from a series dictionary

A series dictionary can be passed to form a DataFrame.

Example:

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:

Fig: Column selection

• Addition of a new column

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

Fig: Adding a new column

• Deleting a column

Columns can be deleted using the del or pop functions.

Example:

Fig: Deleting a column

• Indexing a DataFrame

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

Example:

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:

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:

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:

Fig: DataFrame

Split Data by Groups

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

Example:

Fig: Splitting data into groups

View Groups

Fig: View groups

Python Pandas: Merging

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

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:

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. 

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