Adventures in Machine Learning

Mastering RegEx to Filter Rows in Pandas DataFrame

Filtering Rows in Pandas DataFrame using RegEx

Data manipulation is an essential skill for any data analyst or scientist. One popular tool for data manipulation is the Pandas library.

Pandas is a fast, powerful, flexible, and easy-to-use open-source data analysis and manipulation library. It provides data structures for efficiently storing and manipulating large datasets, and numerous tools for data cleaning, transformation, and analysis.

One of the most commonly used functions in Pandas is filtering rows based on certain conditions. Filtering rows in a Pandas DataFrame is a powerful and efficient way to extract relevant information from large datasets.

In this article, we will focus on how to filter rows using regular expressions (RegEx) in Pandas. Filter rows based on name starting with ‘B’

When working with large datasets, it is often necessary to filter rows based on specific criteria.

One common criterion for filtering rows is to select rows where the name starts with a particular letter. In this case, we will select rows where the name starts with ‘B’.

To filter rows based on name starting with ‘B’, we can use the Pandas `str.startswith()` method. This method returns a Boolean mask indicating whether each element in the specified column starts with the specified substring (‘B’ in this case).

We can then use this mask to select the relevant rows using the Pandas DataFrame indexing operator `[]`. Here’s an example code that demonstrates how to filter rows based on name starting with ‘B’:

“`python

import pandas as pd

df = pd.read_csv(‘people.csv’) # load the dataset

# filter rows where the name starts with ‘B’

mask = df[‘name’].str.startswith(‘B’)

b_names_df = df[mask] # select the relevant rows using the Boolean mask

# print the filtered DataFrame

print(b_names_df)

“`

Filter rows based on name ending with ‘l’

Another common criterion for filtering rows is based on the end of a string. In this case, we will focus on selecting rows where the name ends with the letter ‘l’.

To filter rows based on name ending with ‘l’, we can use the `str.endswith()` method in a similar way to `str.startswith()`. This method returns a Boolean mask indicating whether each element in the specified column ends with the specified substring (‘l’ in this case).

We can then use this mask to select the relevant rows using the Pandas DataFrame indexing operator `[]`. Here’s an example code that demonstrates how to filter rows based on name ending with ‘l’:

“`python

import pandas as pd

df = pd.read_csv(‘people.csv’) # load the dataset

# filter rows where the name ends with ‘l’

mask = df[‘name’].str.endswith(‘l’)

l_names_df = df[mask] # select the relevant rows using the Boolean mask

# print the filtered DataFrame

print(l_names_df)

“`

Filter rows based on name starting with ‘B’ or country starting with ‘C’

In some cases, we may need to filter rows based on multiple criteria. For example, we may want to select rows where the name starts with ‘B’ or the country starts with ‘C’.

To filter rows based on multiple criteria, we can use the `|` (or) operator to combine multiple Boolean masks. In this case, we will use the `str.startswith()` method to create Boolean masks for both name and country, and then combine them using the `|` operator.

We can then use the resulting mask to select the relevant rows using the Pandas DataFrame indexing operator `[]`. Here’s an example code that demonstrates how to filter rows based on name starting with ‘B’ or country starting with ‘C’:

“`python

import pandas as pd

df = pd.read_csv(‘people.csv’) # load the dataset

# filter rows where the name starts with ‘B’ or the country starts with ‘C’

name_mask = df[‘name’].str.startswith(‘B’)

country_mask = df[‘country’].str.startswith(‘C’)

bc_df = df[name_mask | country_mask] # combine the masks using the | (or) operator

# print the filtered DataFrame

print(bc_df)

“`

Filter rows based on name starting with ‘A’ or country ending with ‘o’

Filtering rows based on multiple criteria can be very powerful. In this case, we will select rows where the name starts with ‘A’ or the country ends with ‘o’.

To filter rows based on name starting with ‘A’ or country ending with ‘o’, we can use a combination of the `str.startswith()` and `str.endswith()` methods, and then combine the resulting Boolean masks using the `|` (or) operator. We can then use the resulting mask to select the relevant rows using the Pandas DataFrame indexing operator `[]`.

Here’s an example code that demonstrates how to filter rows based on name starting with ‘A’ or country ending with ‘o’:

“`python

import pandas as pd

df = pd.read_csv(‘people.csv’) # load the dataset

# filter rows where the name starts with ‘A’ or the country ends with ‘o’

name_mask = df[‘name’].str.startswith(‘A’)

country_mask = df[‘country’].str.endswith(‘o’)

ao_df = df[name_mask | country_mask] # combine the masks using the | (or) operator

# print the filtered DataFrame

print(ao_df)

“`

Filter rows based on age starting with ‘2’

Sometimes we may need to filter rows based on numeric values. In this case, we will select rows where the age starts with the digit ‘2’.

To filter rows based on age starting with ‘2’, we can use the `str.startswith()` method after converting the age column to string using the `astype()` method. This is because the `str` accessor is not available for integer columns.

We can then use the resulting Boolean mask to select the relevant rows using the Pandas DataFrame indexing operator `[]`. Here’s an example code that demonstrates how to filter rows based on age starting with ‘2’:

“`python

import pandas as pd

df = pd.read_csv(‘people.csv’) # load the dataset

# filter rows where the age starts with ‘2’

age_str = df[‘age’].astype(str) # convert age column to string

mask = age_str.str.startswith(‘2’)

age2_df = df[mask] # select the relevant rows using the Boolean mask

# print the filtered DataFrame

print(age2_df)

“`

Conclusion

Filtering rows based on certain criteria is a powerful way to extract relevant information from large datasets. In this article, we have looked at how to filter rows using regular expressions (RegEx) in Pandas.

We have covered various subtopics, including filtering rows based on name starting with ‘B’, name ending with ‘l’, name starting with ‘B’ or country starting with ‘C’, name starting with ‘A’ or country ending with ‘o’, and age starting with ‘2’. With this knowledge, you can now easily filter rows in Pandas based on complex criteria.

In this article, we learned how to filter rows in a Pandas DataFrame using regular expressions (RegEx). We explored various subtopics, including filtering rows based on name starting with ‘B’ or ending with ‘l’, or country starting with ‘C’ or ending with ‘o’, as well as age starting with ‘2’.

Filtering rows based on certain criteria is a powerful way to extract relevant information from large datasets, and RegEx provides a flexible and efficient method for doing so in Pandas. By using the knowledge you have gained from this article, you can now easily filter rows in Pandas based on complex criteria, which is a crucial skill for any data analyst or scientist.

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