Caveats and Gotchas in Pandas

What are Caveats in Pandas?

Caveats in Pandas are like warnings. These warnings can occur at any time when we use Pandas. They are unexpected behaviors that may occur when we are using Pandas to work with data. These warnings arise when we handle different types of data. 

So, if we are aware of these caveats, it will help us to avoid getting unexpected or wrong results. 

What are Gotchas in Pandas?

Gotchas in Pandas are like sneaky traps, or we can say they are unseen problems. Same as Cavetas, Gotchas may occur when we are working with data using the Pandas library. These are situations where we will see our code is not working as we have thought. Gotchas in Pandas can lead to errors or unexpected outcomes in our data analysis or manipulation tasks. 

So, if we are aware of these gotchas, it will help us to navigate through tricky situations. We can ensure that our data work is accurate and reliable.

**Now, you might be wondering how caveats and gotchas occur in Pandas.

Examples of Caveats and Gotchas

Let us discuss examples in which we will see they are not working as expected:

Example 1

Let us discuss how caveats occur in Pandas. 

• Python

Python

# Importing Pandas library

import pandas as pd



# Data

dataofninjas = {'Name': ['Narayan', 'Alisha', 'Dhruv', 'Abhinav'],

        'Age': [20, 21, 19, 29]}



# Creating DataFrame

dataframe = pd.DataFrame(dataofninjas)



# Now, if we want to update the age of the person 'Narayan' 

# We will write the following line

dataframe[dataframe['Name'] == 'Narayan']['Age'] = 22


# If we try to print the dataframe we will see the age of 'Narayan' as 22 only

print(dataframe)

# This will happen because of caveats related to chained assignment 

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Output:

Explanation

In this example, we are trying to update the age of a person of the dataofninjas. In this example, the chained assignment first selects a view of the DataFrame of dataofninjas based on the condition dataframe['Name'] == 'Narayan'. Then it tries to update the 'Age' column within that view. However, since this is a view and not the original DataFrame, the update doesn't affect the original data. So, to get the expected result, we can write the following line 

dataframe.loc[dataframe['Name'] == 'Narayan', 'Age'] = 22

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After replacing this line in the previous code, we will see the following output.

Example 2

Let us understand how gotchas occur in Pandas.

• Python

Python

# Importing Pandas library

import pandas as pd


# Data

dataofninjas = {'Name': ['Narayan', 'Alisha', 'Dhruv', 'Abhinav'],

        'Age': [20, 21, 19, 29]}


# Creating DataFrame

originaldataframe = pd.DataFrame(dataofninjas)


# Original DataFrame

print("Original DataFrame before the operation:")

print(originaldataframe)


# Creating copy of the original DataFrame

copydataframe = originaldataframe



# Trying to update the copy DataFrame

copydataframe.loc[0, 'Age'] = 22



# Printing copy DataFrame

print("\nCopy DataFrame after the operation:")

print(copydataframe)



# Printing original DataFrame

print("\nOriginal DataFrame after the operation:")

print(originaldataframe)

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Output:

Explanation

In this example, we have made a copy of the created original DataFrame. Then we tried to perform an update operation on the copy DataFrame. But due to gotchas, our original DataFrame also got affected due to an update operation, and it gave inaccurate results. When we did copydataframe = originaldataframe, it hasn’t copied originaldataframe to copydataframe, and it created a new reference to the same DataFrame object. That's why changes made to one DataFrame are reflected in the other DataFrame. We can deal with gotcha by using copy() method, it will create a true copy of the originaldataframe.

copydataframe = originaldataframe.copy()

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After replacing this line in the previous code, we will see the following output.

** Now, you might be thinking, what are the ways to deal with caveats and gotchas in Pandas?

Ways to Deal With Caveats and Gotchas in Pandas

There are several ways to deal with caveats(warnings) and gotchas(unseen problems) in Pandas, and a few of them are mentioned below:

Using If/Truth Statements with Pandas

When we work with Pandas DataFrames, we might be tempted to use if or truth statements. We can use them to filter and modify data. This approach might work successfully, but still, it has caveats. Suppose we have a DataFrame with ninjasdata and we want to add a new column, ‘Result’ of every ninja. So, we can write the following code:

• Python

Python

# Importing Pandas library

import pandas as pd



# Data

ninjasdata = {'Name': ['Narayan', 'Alisha', 'Dhruv', 'Abhinav', 'Mehak'],

        'Marks': [500, 410, 290, 140, 400]}



# Creating a DataFrame

dataframe = pd.DataFrame(ninjasdata)



# Printing the DataFrame

print("Original DataFrame:")

print(dataframe)


# Adding new column Result based on the marks

dataframe['Result'] = 'Fail' if dataframe['Marks'] < 200 else 'Pass'


# Printing DataFrame

print("\nDataFrame after the operation:")

print(dataframe)

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Output:

Explanation

In this example, we have created a DataFrame of ninjasdata. Then we tried to perform an operation of adding a new column based on the marks of ninjas. Then we executed this code, and we got a ValueError. Pandas doesn't handle the if statement with the entire Series dataframe['Marks'] at once. This is a gotcha that stems from how Pandas treats truth statements in the context of a Series. 

To achieve the desired outcome, we need to use a vectorized approach. We can use apply() or the where() method from the NumPy library. So, we have to rewrite this code:

• Python

Python

# Importing Pandas library

import pandas as pd



# Importing NumPy library

import numpy as np



# Data

ninjasdata = {'Name': ['Narayan', 'Alisha', 'Dhruv', 'Abhinav', 'Mehak'],

        'Marks': [500, 410, 290, 140, 400]}



# Creating a DataFrame

dataframe = pd.DataFrame(ninjasdata)



# Printing the DataFrame

print("Original DataFrame:")

print(dataframe)



# Adding new column Result based on the marks

dataframe['Result'] = np.where(dataframe['Marks'] < 200, 'Fail', 'Pass')


# Printing DataFrame

print("\nDataFrame after the operation:")

print(dataframe)

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After executing this code, we will see our desired output:

Using isin() Method

The isin() method is used in Pandas to filter the data based if values present in a list-like object. Using this method, it's important to be aware of its behavior to avoid unexpected results.  Suppose we have a DataFrame with ninjasdata and we want to filter the DataFrame to include rows where the ‘Result’ of the ninja is either 'Pass' or 'Fail'.  So, we can write the following code:

• Python

Python

# Importing Pandas library

import pandas as pd



# Data

ninjasdata = {'Name': ['Narayan', 'Alisha', 'Dhruv', 'Abhinav', 'Mehak'],

        'Result': ['Pass', 'Pass', 'Not Appeared in Exams', 'Fail', 'Pass']}



# Creating a DataFrame

dataframe = pd.DataFrame(ninjasdata)



# Printing the DataFrame

print("Original DataFrame:")

print(dataframe)



# Filtering the results based on a condition

filtereddataframe = dataframe[dataframe['Result'].isin('Pass', 'Fail')]



# Printing Filtered DataFrame

print("\nDataFrame after the operation:")

print(filtereddataframe)

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Output:

Explanation

In this example, we have created a DataFrame of ninjasdata. Then we tried to perform an operation to filtereddataframe, based on the results of ninjas. Then we executed this code. We got a TypeError because the isin() method expects a single list-like object, and it is a gotcha. We cannot pass multiple arguments to it.

We can deal with this gotcha by passing a single list-like object with the values we want to check for. So, we have to rewrite this code:

• Python

Python

# Importing Pandas library

import pandas as pd


# Data

ninjasdata = {'Name': ['Narayan', 'Alisha', 'Dhruv', 'Abhinav', 'Mehak'],

        'Result': ['Pass', 'Pass', 'Not Appeared in Exams', 'Fail', 'Pass']}



# Creating a DataFrame

dataframe = pd.DataFrame(ninjasdata)



# Printing the DataFrame

print("Original DataFrame:")

print(dataframe)


# Filtering the results based on a condition

filtereddataframe = dataframe[dataframe['Result'].isin(['Pass', 'Fail'])]


# Printing Filtered DataFrame

print("\nDataFrame after the operation:")

print(filtereddataframe)

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After executing this code, we will see the desired output:

Using Bitwise Boolean Operators

There are several bitwise boolean operators, such as & (and), | (or), and ~ (not). These all are used to combine boolean conditions for filtering data in Pandas. In Pandas, these operators come up with some caveats to consider. Suppose we have a DataFrame of ninjasdata. Now, we want to filter the DataFrame to include rows where marks of ninjas are between 250 and 600. So, we can write the following code:

• Python

Python

# Importing Pandas library

import pandas as pd


# Data

ninjasdata = {'Name': ['Narayan', 'Alisha', 'Dhruv', 'Abhinav', 'Mehak'],

        'Marks': [500, 410, 290, 140, 400]}



# Creating a DataFrame

dataframe = pd.DataFrame(ninjasdata)



# Printing the DataFrame

print("Original DataFrame:")

print(dataframe)


# Filtering the marks based on a condition

filtereddataframe = dataframe[dataframe['Marks'] >= 300 & dataframe['Marks'] <= 600]



# Printing DataFrame

print("\nDataFrame after the operation:")

print(filtereddataframe)

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Output:

Explanation

In this example, we have created a DataFrame of ninjasdata. Then we tried to perform an operation to filtereddataframe, based on the marks of ninjas. Then we executed this code, and we got a ValueError because of operator precedence. The & operator is evaluated before the comparison operators, that’s why it causes unexpected behavior. 

The gotcha in this code is that operator precedence matters. In Python, bitwise boolean operators have higher precedence compared to comparison operators. To overcome this gotcha, we can use parentheses to ensure the correct order of operations. So, we have to rewrite this code:

• Python

Python

# Importing Pandas library

import pandas as pd



# Data

ninjasdata = {'Name': ['Narayan', 'Alisha', 'Dhruv', 'Abhinav', 'Mehak'],

        'Marks': [500, 410, 290, 140, 400]}



# Creating a DataFrame

dataframe = pd.DataFrame(ninjasdata)


# Printing the DataFrame

print("Original DataFrame:")

print(dataframe)


# Filtering the marks based on a condition

filtereddataframe = dataframe[(dataframe['Marks'] >= 300) & (dataframe['Marks'] <= 600)]


# Printing Filtered DataFrame

print("\nDataFrame after the operation:")

print(filtereddataframe)

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After executing this code, we will see the desired output:

Frequently Asked Questions

Are caveats and gotchas avoidable?

Caveats and gotchas are not entirely avoidable in complex programming tools like Pandas. But if we have a better understanding of them, then we can greatly reduce the chances of encountering unexpected behavior.

Are there any tools to detect and handle caveats and gotchas automatically?

There are several tools available to detect caveats and gotchas automatically. There are tools like linting libraries that can help us to detect. 

Can caveats and gotchas impact performance?

In certain cases, inefficient code that doesn't account for caveats and gotchas might lead to suboptimal performance. It can also give us unexpected outcomes.

Are there specific scenarios where caveats are more likely to occur?

Caveats are more likely to occur when dealing with complex transformations. It can also occur when merging datasets, handling missing data, and working with datetime and index-related operations.

Conclusion

In this blog, we have discussed the caveats and gotchas in Pandas. We have also discussed ways to deal with caveats and gotchas with the help of examples. If you want to learn more about the Pandas in Python, then you can check out our blogs:

• How to Install Pandas in Python?
   
• Import Pandas as pd
   
• Pandas Read Excel
   
• Pandas dataframe.shift()
   
• Pandas Profiling in Python
   
• ewm() Method in Pandas
   

We hope this blog helps you to get knowledge about the caveats and gotchas in Pandas. You can refer to our guided paths on the Codestudio platform. You can also consider our paid courses such as DSA in Python to give your career an edge over others! 

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Happy Coding!!