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    Pandas loc[] Multiple Conditions

    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).

    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.

    Key Points –

    <a id="h-quick-examples-of-loc-multiple-conditions"></a>

    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)

    <a id="h-usage-of-pandas-loc-multiple-conditions"></a>

    Usage of Pandas loc[] Multiple Conditions

    Use logical operators such as & (AND), | (OR), and ~ (NOT), making sure to enclose each condition in parentheses. The loc[] accessor in Pandas makes it easy to filter rows using multiple conditions in a clear and readable way.

    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.

    <a id="using-conditions-loc-and-iloc"></a>

    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.

    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

    <a id="h-complete-examples"></a>

    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)

    <a id="h-faq-on-pandas-loc-multiple-conditions"></a>

    FAQ on Pandas loc[] Multiple Conditions

    To apply multiple conditions with loc[], you can use logical operators like & (AND), | (OR), and ~ (NOT). Each condition should be enclosed in parentheses.

    Parentheses are necessary to ensure that each condition is evaluated properly before applying the logical operators. Without them, Python will throw an error or not evaluate the conditions correctly.

    You can use more than two conditions with loc[] in Pandas. You can combine as many conditions as needed using logical operators (& for AND, | for OR, ~ for NOT). Each condition must be enclosed in parentheses.

    To negate a condition with loc[], you can use the ~ (tilde) operator. The tilde negates the boolean condition, selecting rows where the condition is not true.

    You can assign values to rows filtered by multiple conditions using loc[] in Pandas. You can use loc[] to select the rows that meet specific conditions, and then assign new values to those rows or specific columns.

    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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