Pandas is a comprehensive Python library used extensively for data manipulation and analysis. In data analysis, extracting data from a table based on another column is a common task. In this article, we will examine how to derive column values based on another column using Pandas.

Understanding the Problem

Let’s say you have a pandas DataFrame with columns “A”, “B”, and “C”, and you want to extract the values in column “C” where the value in column “A” equals a certain value, let’s say “X”.

You can do this using boolean indexing like this:

				
					import pandas as pd

# create a sample DataFrame
df = pd.DataFrame({'A': ['X', 'Y', 'Z'], 'B': [1, 2, 3], 'C': [10, 20, 30]})

# extract the value in column C where the value in column A equals 'X'
result = df.loc[df['A'] == 'X', 'C'].values[0]

print(result)

Output:

				
					# 10

In this example, we used the .loc accessor to select the rows where the value in column “A” equals “X”, and then selected the value in column “C” using the .loc accessor again. The .values[0] at the end is used to extract the actual value from the resulting DataFrame.

Example 1: Extract Column Values Based on One Condition Being Met

Let’s say you have a pandas DataFrame with columns “A”, “B”, and “C”, and you want to extract the values in column “B” where the value in column “A” equals a certain value, let’s say “X”.

You can use boolean indexing like this:

				
					import pandas as pd

# create a sample DataFrame
df = pd.DataFrame({'A': ['X', 'Y', 'Z'], 'B': [1, 2, 3], 'C': [10, 20, 30]})

# extract the values in column B where the value in column A equals 'X'
result = df.loc[df['A'] == 'X', 'B'].tolist()

print(result)

Output:

				
					# [1]

In this example, we used the .loc accessor to select the rows where the value in column “A” equals “X”, and then selected the values in column “B” using the .loc accessor again. The .tolist() at the end is used to convert the resulting pandas Series object to a Python list.

Example 2: Extract Column Values Based on One of Several Conditions Being Met

Let’s say you have a pandas DataFrame with columns “A”, “B”, and “C”, and you want to extract the values in column “B” where the value in column “A” equals one of several values, let’s say “X” or “Y”.

You can use boolean indexing with the | operator (which stands for “or”) like this:

				
					import pandas as pd

# create a sample DataFrame
df = pd.DataFrame({'A': ['X', 'Y', 'Z'], 'B': [1, 2, 3], 'C': [10, 20, 30]})

# extract the values in column B where the value in column A equals 'X' or 'Y'
result = df.loc[(df['A'] == 'X') | (df['A'] == 'Y'), 'B'].tolist()

print(result)

Output:

				
					# [1, 2]

In this example, we used the .loc accessor to select the rows where the value in column “A” equals “X” or “Y”, and then selected the values in column “B” using the .loc accessor again. The | operator is used to combine two boolean conditions with an “or” relationship. The .tolist() at the end is used to convert the resulting pandas Series object to a Python list.

Example 3: Extract Column Values Based on Several Conditions Being Met

Let’s say you have a pandas DataFrame with columns “A”, “B”, and “C”, and you want to extract the values in column “B” where the value in column “A” equals one value, and the value in column “C” equals another value.

You can use boolean indexing with the & operator (which stands for “and”) like this:

				
					import pandas as pd

# create a sample DataFrame
df = pd.DataFrame({'A': ['X', 'Y', 'Z'], 'B': [1, 2, 3], 'C': [10, 20, 30]})

# extract the values in column B where the value in column A equals 'X' and the value in column C equals 10
result = df.loc[(df['A'] == 'X') & (df['C'] == 10), 'B'].tolist()

print(result)

Output:

				
					# [1]

In this example, we used the .loc accessor to select the rows where the value in column “A” equals “X” and the value in column “C” equals 10, and then selected the values in column “B” using the .loc accessor again. The & operator is used to combine two boolean conditions with an “and” relationship. The .tolist() at the end is used to convert the resulting pandas Series object to a Python list.

Wrap up

To learn more about the Pandas shift method, check out the official documentation here:
https://pandas.pydata.org/docs


Thanks for reading. Happy coding!