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.
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
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)
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.
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.
Fig: Access data in a series
To retrieve data using labels, we enter the label value.
Fig: Retrieving data by label name
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)
Fig: Empty DataFrame
Basic Operations on DataFrames
- Create a DataFrame from lists
A DataFrame can be created using a list:
Fig: 2-D DataFrame
- Creating a DataFrame from a series dictionary
A series dictionary can be passed to form a DataFrame.
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.
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.
Fig: Deleting a column
- Indexing a DataFrame
The iloc() method is used for integer-based indexing.
Python Pandas Sorting
There are two types of sorting available in pandas. They are:
- By label
- By actual value
The sort_index() method is used to sort data in pandas. You pass the axis arguments and order of the 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.
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.
Split Data by Groups
Let us see how grouping objects can be used in DataFrames.
Fig: Splitting data into 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.
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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