How to Select Rows from Pandas DataFrame
Pandas is a popular data manipulation library built on top of the Python NumPy library. It provides two main data structures: Series, which represents one-dimensional labeled data, and DataFrame, which represents two-dimensional tabular data. The DataFrame is capable of handling both homogeneous and heterogeneous data, making it versatile for various data analysis tasks. With Pandas DataFrame, you can easily perform essential operations on rows, such as selecting, deleting, adding, and renaming, allowing for efficient data manipulation and exploration.
Create a Pandas DataFrame with data
Selecting rows using 
You can use square brackets with row index or row labels to access specific rows from a Pandas DataFrame. For example, df[2:5] would retrieve rows with index 2, 3, and 4, and df.loc['label'] would retrieve the row with the label 'label'. This indexing method allows you to extract and work with specific rows of your DataFrame efficiently.
When using square brackets to access data from a Pandas DataFrame, you can specify the column names as well. For example, df['column_name'] would retrieve the entire column with the name 'column_name', and df[['column1', 'column2']] would retrieve a subset of the DataFrame containing only the 'column1' and 'column2' columns. This allows you to select specific columns of interest and perform operations on them.
Selecting rows using loc
When using the loc method to access data from a Pandas DataFrame, you can specify both row and column labels. The syntax for using loc is df.loc[row_label, column_label].
For example, df.loc[3, 'column_name'] would retrieve the value at row 3 and the specified column with the name 'column_name'. Similarly, df.loc[:, ['column1', 'column2']] would retrieve all rows and only the 'column1' and 'column2' columns.
Using loc provides more flexibility in selecting specific rows and columns based on their labels, making it useful for various data selection tasks in Pandas.
Select rows based on condition using loc
Using 'loc' and '!='
Combine multiple conditions with & operator
Selected columns using loc
Using loc and isin()
Selected column using loc and isin()
In Pandas DataFrame, you can select rows using different methods like integer indexing, slicing, and boolean indexing. The iloc and loc methods provide ways to access rows based on integer positions and labels, respectively, offering flexibility in row selection.