Pandas loc[] Multiple Conditions

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  • Post last modified:May 29, 2024
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You are currently viewing Pandas loc[] Multiple Conditions

To select rows based on multiple conditions, use the Pandas loc[] attribute. The loc[] function in pandas allows you to select data based on labels or a boolean array. When dealing with multiple conditions, you can combine them using logical operators like & (and), | (or), and ~ (not).

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A Pandas DataFrame, a structured data format with labeled rows and columns, represents two-dimensional data. When you select specific columns from a DataFrame yields a fresh DataFrame containing solely the chosen columns from the original. In this article, I will explain pandas loc[] with multiple conditions.

Quick Examples of loc[] Multiple Conditions

Below are some quick examples of Pandas loc[] multiple conditions.


# Quick examples of loc[] multiple conditions

# Example 1 Using loc[] with multiple conditions
df2=df.loc[(df['Discount'] >= 1000) & (df['Discount'] <= 2000)]

# Example 2
df2=df.loc[(df['Discount'] >= 1200) | (df['Fee'] >= 23000 )]
print(df2)

First, let’s create a pandas DataFrame.


# Create DataFrame
import pandas as pd
technologies = {
    'Courses':["Spark","PySpark","Hadoop","Python","pandas"],
    'Fee' :[20000,25000,26000,22000,24000],
    'Duration':['30day','40days','35days','40days','60days'],
    'Discount':[1000,2300,1200,2500,2000]
              }
index_labels=['r1','r2','r3','r4','r5']
df = pd.DataFrame(technologies,index=index_labels)
print("Create DataFrame:\n", df)

# Outputs:
# r1    Spark  20000    30day      1000
# r2  PySpark  25000   40days      2300
# r3   Hadoop  26000   35days      1200
# r4   Python  22000   40days      2500
# r5   pandas  24000   60days      2000

Yields below output.

Pandas loc multiple conditions

Using loc[] by Multiple Conditions

By using the loc[] attribute you can get selected or filtered rows from DataFrame based on multiple conditions. Here, you can specify the multiple conditions using the & operator. Make sure you surround each condition with the brace. Not using this will get you incorrect results.


# Using loc[] by Multiple Conditions
df2=df.loc[(df['Discount'] >= 1000) & (df['Discount'] <= 2000)]
print("Get selected rows after applying multiple conditions:\n", df2)

Yields below output.

Pandas loc multiple conditions

let’s look at another example, here, we can specify the multiple conditions using | (or) operator.


Using loc[] by multiple conditions
df2=df.loc[(df['Discount'] >= 1200) | (df['Fee'] >= 23000)]
print("Get selected rows after applying multiple conditions:\n", df2)

Yields below output.


# Output:
# Get selected rows after applying multiple conditions:
    Courses    Fee Duration  Discount
r2  PySpark  25000   40days      2300
r3   Hadoop  26000   35days      1200
r4   Python  22000   40days      2500
r5   pandas  24000   60days      2000

Complete Examples 


import pandas as pd
technologies = {
    'Courses':["Spark","PySpark","Hadoop","Python","pandas"],
    'Fee' :[20000,25000,26000,22000,24000],
    'Duration':['30day','40days','35days','40days','60days'],
    'Discount':[1000,2300,1200,2500,2000]
              }
index_labels=['r1','r2','r3','r4','r5']
df = pd.DataFrame(technologies,index=index_labels)
print(df)

# Example 1 - Using loc[] with multiple conditions
df2=df.loc[(df['Discount'] >= 1000) & (df['Discount'] <= 2000)]
print(df2)

# Example 2
df2=df.loc[(df['Discount'] >= 1200) | (df['Fee'] >= 23000 )]
print(df2)

Conclusion

In this article, you have learned about Pandas loc[] property to filter or select DataFrame rows based on multiple conditions. Also explained how we can specify the multiple conditions using logical operators like, and(&) and or(|).

Happy Learning !!

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