How to Select Rows from Pandas DataFrame

In this short guide, you’ll see the steps to select rows from Pandas DataFrame based on the conditions specified.

Steps to Select Rows from Pandas DataFrame

Step 1: Gather your data

Firstly, you’ll need to gather your data. Here is an example of a data gathered about boxes:

Step 2: Create a DataFrame

Once you have your data ready, you’ll need to create a DataFrame to capture that data in Python.

For our example, you may use the code below to create a DataFrame:

import pandas as pd

data = {'Color': ['Green', 'Green', 'Green', 'Blue', 'Blue', 'Red', 'Red', 'Red'],
        'Shape': ['Rectangle', 'Rectangle', 'Square', 'Rectangle', 'Square', 'Square', 'Square', 'Rectangle'],
        'Price': [10, 15, 5, 5, 10, 15, 15, 5]
        }

df = pd.DataFrame(data)

print(df)

Run the code in Python and you’ll see this DataFrame:

   Color      Shape  Price
0  Green  Rectangle     10
1  Green  Rectangle     15
2  Green     Square      5
3   Blue  Rectangle      5
4   Blue     Square     10
5    Red     Square     15
6    Red     Square     15
7    Red  Rectangle      5

Step 3: Select Rows from Pandas DataFrame

You can use the following logic to select rows from Pandas DataFrame based on specified conditions:

df.loc[df[‘column name’] condition]

For example, if you want to get the rows where the color is green, then you’ll need to apply:

df.loc[df[‘Color’] == ‘Green’]

Where:

• Color is the column name
• Green is the condition

And here is the full Python code for our example:

import pandas as pd

data = {'Color': ['Green', 'Green', 'Green', 'Blue', 'Blue', 'Red', 'Red', 'Red'],
        'Shape': ['Rectangle', 'Rectangle', 'Square', 'Rectangle', 'Square', 'Square', 'Square', 'Rectangle'],
        'Price': [10, 15, 5, 5, 10, 15, 15, 5]
        }

df = pd.DataFrame(data)

select_color = df.loc[df['Color'] == 'Green']

print(select_color)

Once you run the code, you’ll get the rows where the color is green:

   Color      Shape  Price
0  Green  Rectangle     10
1  Green  Rectangle     15
2  Green     Square      5

Additional Examples of Selecting Rows from Pandas DataFrame

Let’s now review additional examples to get a better sense of selecting rows from Pandas DataFrame.

Example 1: Select rows where the price is equal or greater than 10

To get all the rows where the price is equal or greater than 10, you’ll need to apply this condition:

df.loc[df[‘Price’] >= 10]

And this is the complete Python code:

import pandas as pd

data = {'Color': ['Green', 'Green', 'Green', 'Blue', 'Blue', 'Red', 'Red', 'Red'],
        'Shape': ['Rectangle', 'Rectangle', 'Square', 'Rectangle', 'Square', 'Square', 'Square', 'Rectangle'],
        'Price': [10, 15, 5, 5, 10, 15, 15, 5]
        }

df = pd.DataFrame(data)

select_price = df.loc[df['Price'] >= 10]

print(select_price)

Run the code, and you’ll get all the rows where the price is equal or greater than 10:

   Color      Shape  Price
0  Green  Rectangle     10
1  Green  Rectangle     15
4   Blue     Square     10
5    Red     Square     15
6    Red     Square     15

Example 2: Select rows where the color is green AND the shape is rectangle

Now the goal is to select rows based on two conditions:

• Color is green; and
• Shape is rectangle

You may then use the & symbol to apply multiple conditions. In our example, the code would look like this:

df.loc[(df[‘Color’] == ‘Green’) & (df[‘Shape’] == ‘Rectangle’)]

Putting everything together:

import pandas as pd

data = {'Color': ['Green', 'Green', 'Green', 'Blue', 'Blue', 'Red', 'Red', 'Red'],
        'Shape': ['Rectangle', 'Rectangle', 'Square', 'Rectangle', 'Square', 'Square', 'Square', 'Rectangle'],
        'Price': [10, 15, 5, 5, 10, 15, 15, 5]
        }

df = pd.DataFrame(data)

color_and_shape = df.loc[(df['Color'] == 'Green') & (df['Shape'] == 'Rectangle')]

print(color_and_shape)

Run the code and you’ll get the rows with the green color and rectangle shape:

   Color      Shape  Price
0  Green  Rectangle     10
1  Green  Rectangle     15

Example 3: Select rows where the color is green OR the shape is rectangle

You can also select the rows based on one condition or another. For instance, you can select the rows if the color is green or the shape is rectangle.

To achieve this goal, you can use the | symbol as follows:

df.loc[(df[‘Color’] == ‘Green’) | (df[‘Shape’] == ‘Rectangle’)]

And here is the complete Python code:

import pandas as pd

data = {'Color': ['Green', 'Green', 'Green', 'Blue', 'Blue', 'Red', 'Red', 'Red'],
        'Shape': ['Rectangle', 'Rectangle', 'Square', 'Rectangle', 'Square', 'Square', 'Square', 'Rectangle'],
        'Price': [10, 15, 5, 5, 10, 15, 15, 5]
        }

df = pd.DataFrame(data)

color_or_shape = df.loc[(df['Color'] == 'Green') | (df['Shape'] == 'Rectangle')]

print(color_or_shape)

Here is the result, where the color is green or the shape is rectangle:

   Color      Shape  Price
0  Green  Rectangle     10
1  Green  Rectangle     15
2  Green     Square      5
3   Blue  Rectangle      5
7    Red  Rectangle      5

Example 4: Select rows where the price is not equal to 15

You can use the combination of symbols != to select the rows where the price is not equal to 15:

df.loc[df[‘Price’] != 15]

import pandas as pd

data = {'Color': ['Green', 'Green', 'Green', 'Blue', 'Blue', 'Red', 'Red', 'Red'],
        'Shape': ['Rectangle', 'Rectangle', 'Square', 'Rectangle', 'Square', 'Square', 'Square', 'Rectangle'],
        'Price': [10, 15, 5, 5, 10, 15, 15, 5]
        }

df = pd.DataFrame(data)

not_equal_to = df.loc[df['Price'] != 15]

print(not_equal_to)

Once you run the code, you’ll get all the rows where the price is not equal to 15:

   Color      Shape  Price
0  Green  Rectangle     10
2  Green     Square      5
3   Blue  Rectangle      5
4   Blue     Square     10
7    Red  Rectangle      5

Finally, the following source provides additional information about indexing and selecting data.