Pandas: Data Analysis and Manipulation in Python

    python-logo

    Pandas is a Python library that provides powerful data analysis and manipulation capabilities. It is widely used in the fields of data science, machine learning, and finance. In this post, we will explore the basics of Pandas and its key features.

    Data Structures in Pandas

    Pandas provides two primary data structures: Series and DataFrame. A Series is a one-dimensional labeled array that can hold any data type. A DataFrame is a two-dimensional labeled data structure with columns of potentially different types. Here are some examples:

    import pandas as pd
# create a Series
my_series = pd.Series([1, 2, 3, 4, 5])
print(my_series)
create a DataFrame
my_data = {'name': ['John', 'Mary', 'Alex', 'Jane'], 'age': [25, 32, 18, 47]}
my_dataframe = pd.DataFrame(my_data)
print(my_dataframe)

    In this example, we import the Pandas library and create a Series of integers and a DataFrame of names and ages. We then print out the Series and DataFrame.

    Data Manipulation in Pandas

    Pandas provides a wide range of functions and methods for manipulating and analyzing data. Here are some common ones:

    • head(): Returns the first n rows of the DataFrame
    • tail(): Returns the last n rows of the DataFrame
    • describe(): Generates descriptive statistics of the DataFrame
    • sort_values(): Sorts the DataFrame by a specified column
    • groupby(): Groups the DataFrame by a specified column
    • apply(): Applies a function to each row or column of the DataFrame

    Here is an example:

    import pandas as pd
# create a DataFrame
my_data = {'name': ['John', 'Mary', 'Alex', 'Jane'], 'age': [25, 32, 18, 47], 'gender': ['M', 'F', 'M', 'F']}
my_dataframe = pd.DataFrame(my_data)
print("Original DataFrame:\n", my_dataframe)
print("First 2 rows:\n", my_dataframe.head(2))
print("Last 2 rows:\n", my_dataframe.tail(2))
print("Descriptive statistics:\n", my_dataframe.describe())
print("Sorted by age:\n", my_dataframe.sort_values('age'))
print("Grouped by gender:\n", my_dataframe.groupby('gender').size())
print("Applied function:\n", my_dataframe.apply(lambda x: x['name'].upper(), axis=1))

    In this example, we create a DataFrame of names, ages, and genders. We then demonstrate various data manipulation operations on the DataFrame, including selecting the first and last few rows, generating descriptive statistics, sorting, grouping, and applying a function to each row.

    Conclusion

    Pandas is a powerful library for data analysis and manipulation in Python. Its two primary data structures, Series and DataFrame, provide flexible ways to store and analyze data. Pandas also provides a wide range of functions and methods for manipulating and analyzing data, such as selecting rows and columns, sorting, grouping, and applying functions to data. With Pandas, data scientists and analysts can quickly and easily explore and manipulate data, making it an essential tool in the field of data science.