Don’t Delay Learning These Pandas Functions as a Beginner in Python

Pandas is a Python library used for data analysis. It provides high-level data structures and operations for working with structured (tabular, multidimensional, potentially heterogeneous) and time series data. Pandas is built on top of the NumPy library, which provides fast and efficient numerical array manipulation. It is a powerful data manipulation library in Python that is widely used in data science and analysis. It provides a variety of functions that make it easy to work with data in a tabular format.

We are going to look at some of the most important pandas functions that will help your learning journey as a beginner.

1. read_csv()

The read_csv() function is used to read data from a CSV file and create a pandas DataFrame. It is a versatile function that can handle a variety of file formats and data types. You can specify various parameters such as delimiter, header, and column names to customize the output.

2. head()

The head() function is used to display the first few rows of a DataFrame. It is a quick way to get a sense of the data and check if it has been loaded correctly. By default, it displays the first five rows, but you can specify the number of rows to display using the n parameter.

3. tail()

The tail() function is similar to the head() function, but it displays the last few rows of a DataFrame. It is useful when you want to check the end of the data or if you want to see if there are any missing values.

4. info()

The info() function is used to get a summary of the DataFrame, including the number of rows and columns, data types, and memory usage. It is a useful function to check if the data has been loaded correctly and to identify any potential issues such as missing values or data types.

5. describe()

The describe() function is used to get a statistical summary of the DataFrame. It provides information such as the mean, standard deviation, minimum, and maximum values for each column. It is a quick way to get an overview of the data and identify any outliers or anomalies.

6. drop()

The drop() function is used to remove rows or columns from a DataFrame. It is a useful function when you want to remove irrelevant or redundant data. You can specify the axis parameter to indicate whether you want to remove rows or columns.

7. groupby()

The groupby() function is used to group data by one or more columns and apply a function to each group. It is a powerful function that can be used for various data analysis tasks such as aggregation, filtering, and transformation.

8. merge()

The merge() function is used to combine two or more DataFrames based on a common column. It is a useful function when you want to combine data from different sources or perform a join operation.

9. DataFrame()

This function creates a Pandas DataFrame from a variety of data sources, such as a list, a dictionary, or a NumPy array.

10. loc[]

This function is used to select rows and columns by label.

11. iloc[]

The i in iloc stands for integer. This function is used to select rows and columns by integer location.

12. groupby()

This function groups DataFrame rows by a common value and performs operations on the groups.

13. agg()

This function applies a function to each group in a DataFrame.

14. plot()

This function plots the DataFrame data.

In conclusion, pandas is a powerful library that provides a variety of functions to work with data in a tabular format. The functions we have discussed are some of the most important ones that you should know. Once you have mastered these functions, you can perform various data analysis tasks efficiently and effectively.