Extracting Numbers by Position in Pandas DataFrame

In this article, we will explore how to extract specific numbers from a column of a Pandas DataFrame. We will cover the use of various methods to achieve this task, including using the .apply() method and list comprehensions.

Introduction

When working with DataFrames, it is often necessary to perform data cleaning or preprocessing tasks. One such task is extracting specific numbers from a column of the DataFrame. In this article, we will explore how to use Pandas to extract numbers by position in a column.

Using .apply() Method

One way to achieve this task is by using the .apply() method. The .apply() method applies a function to each element of a Series or DataFrame.

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({"col1": ["01139290201001", "01139290101001", "01139290201002", "01139ÅÊ21020", "01139ÅÊ21013", "01139ÅÊ11008"]})

# Use .apply() to extract numbers by position
df["col2"] = df["col1"].apply(lambda x: x[:7] if x.isdigit() else x[:5]+x[9:11] )

print(df)

Output:

In this example, the .apply() method is used to apply a lambda function to each element of the “col1” column. The lambda function checks if the element is a digit using the isdigit() method. If it is, it extracts the first 7 characters; otherwise, it extracts the first 5 characters and the last two characters.

List Comprehension

Another way to achieve this task is by using list comprehensions.

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({"col1": ["01139290201001", "01139290101001", "01139290201002", "01139ÅÊ21020", "01139ÅÊ21013", "01139ÅÊ11008"]})

# Use list comprehension to extract numbers by position
df["col2"] = [x[:7] if x.isdigit() else x[:5]+x[9:11] for x in df["col1"]]

print(df)

Output:

In this example, the list comprehension is used to create a new column “col2” that contains the extracted numbers.

Why Does it Cause NaN Values?

The .apply() method and list comprehensions can cause NaN values in certain cases. This happens because the isdigit() method returns False for non-digit characters, and when these characters are not digits, the lambda function or list comprehension assigns a value to the “col2” column that is not a number.

To avoid this issue, we need to modify the lambda function or list comprehension to handle non-digit characters correctly. We can do this by checking if the element is a digit before extracting the numbers.

Handling Non-Digit Characters

We can modify the lambda function or list comprehension to handle non-digit characters as follows:

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({"col1": ["01139290201001", "01139290101001", "01139290201002", "01139ÅÊ21020", "01139ÅÊ21013", "01139ÅÊ11008"]})

# Use .apply() to extract numbers by position
df["col2"] = df["col1"].apply(lambda x: str(x[:7] if x.isdigit() else x[:5]+x[9:11]) )

print(df)

Output:

In this modified example, the str() function is used to convert the extracted numbers to strings. This ensures that the values in the “col2” column are always strings.

Conclusion

In this article, we explored how to extract specific numbers from a column of a Pandas DataFrame using various methods. We covered the use of the .apply() method and list comprehensions to achieve this task. We also discussed why these methods can cause NaN values and provided modifications to handle non-digit characters correctly. By understanding how to extract numbers by position in a Pandas DataFrame, we can perform data cleaning or preprocessing tasks more efficiently.

Last modified on 2023-10-10