Day 5-7 of 100 Days of ML Code: Data Manipulation with Pandas

"Embrace the struggle, trust the process, and persist with effort."

To start using the Pandas library, we need to import it into our Python environment. For this example, I downloaded a Shop Customer Data dataset from Kaggle that provides a detailed analysis of an imaginary shop's ideal customers. To read the CSV file into our Pandas DataFrame, we can use the pd.read_csv() function.

Indexing

Indexing in Pandas refers to the process of selecting specific rows and/or columns from a Pandas DataFrame or Series. There are several ways to perform indexing in Pandas:

iloc(): This method allows you to select rows and columns by integer location.

loc(): This method allows you to select rows and columns by label or boolean mask.

Boolean Indexing: This method allows you to filter a DataFrame by a boolean condition.

Filtering

Filtering in Pandas refers to the process of selecting a subset of data from a DataFrame based on certain conditions.

Updating Rows and Columns

Updating rows and columns in a Pandas DataFrame involves changing the values of specific cells, rows, or columns.

Updating a specific cell:

Updating a specific row:

Updating a specific column:

In Pandas, there are two methods along with filter that are commonly used for transforming data:

apply: applies a function to a DataFrame or a Series along an axis.

map: applies a function to each element of a Series.

Adding/ Removing Rows and Columns

append(): You can use this method to add one or more rows to an existing DataFrame.

drop(): You can use this method to remove one or more rows from a DataFrame.

Sorting Data

Sorting data is an important operation in data analysis. In Pandas, you can sort data using the sort_values() method.

Grouping and Aggregating Data

Grouping and aggregating data is a common operation in data analysis. In Pandas, you can group data using the groupby() method and aggregate it using the agg() method.