Adventures in Machine Learning

Mastering Pandas Data Frame Combination in Python

Append: Combining Pandas DataFrames

When working with data in Python, Pandas is a powerful tool that facilitates data processing and manipulation. One of the most common tasks when working with data is combining data frames.

Data frames could have come from different sources and may have different structures. Combining these data frames at the right juncture can provide insight that wasn’t visible before.

In this article, we will focus on how to append Pandas data frames and provide examples to show how this can be achieved.

Syntax for Appending Two DataFrames

To combine Pandas data frames, one simple way is appending. Appending data frames involves combining two data frames side by side where all the column labels are the same or align perfectly.

Therefore, before appending data frames, one needs to ensure the columns in both frames are aligned correctly. Below is the syntax for appending Pandas data frames:

new_df = pd.concat([df1, df2])

The above code is the starting point for appending data frames.

Here, both df1 and df2 are the two data frames being combined, and new_df represents the data frame that has been aligned. The new data frame is then assigned to a new variable name, in this case, new_df.

The pd.concat() function used in the above code belongs to pandas and takes in an array of data frames to be appended. The default axis is zero, so Pandas concatenates the two data frames vertically.

Consider an example where two data frames (df1 and df2) need to be combined. The code for achieving this would look like this:

“` python

import pandas as pd

# defining data frames

df1 = pd.DataFrame({‘A’: [1, 2, 3], ‘B’: [4, 5, 6]})

df2 = pd.DataFrame({‘A’: [10, 20, 30], ‘B’: [40, 50, 60]})

# appending data frames

new_df = pd.concat([df1, df2])

print(new_df)

“`

The output in this case would look like:

“`

import pandas as pd

# defining data frames

df1 = pd.DataFrame({‘A’: [1, 2, 3], ‘B’: [4, 5, 6]})

df2 = pd.DataFrame({‘A’: [10, 20, 30], ‘B’: [40, 50, 60]})

#appending data frames

new_df = pd.concat([df1, df2], ignore_index=True)

# printing new dataframe

print(new_df)

“`

Our new data frame will contain six rows and two columns with the following information:

“`

A B

0 1 4

1 2 5

2 3 6

3 10 40

4 20 50

5 30 60

“`

The ignore_index=True attribute tells pandas to reset the index of the new_data frame. This comes in handy in cases where the data in the two data frames may overlap.

Example 2:

Appending More Than Two DataFrames

It is often necessary to append more than just two data frames. In such a scenario, the logic remains the same; append two data frames and then join to the rest.

A simple example can better illustrate this scenario:

import pandas as pd

#defining data frames

df1 = pd.DataFrame({‘A’: [1, 2, 3], ‘B’: [4, 5, 6]})

df2 = pd.DataFrame({‘A’: [10, 20, 30], ‘B’: [40, 50, 60]})

df3 = pd.DataFrame({‘A’: [100, 200, 300], ‘B’: [400, 500, 600]})

#appending data frames

new_df = pd.concat([df1, df2, df3], ignore_index=True)

#printing new dataframe

print(new_df)

The output in this example will look like this:

“`

import pandas as pd

#defining data frames

df1 = pd.DataFrame({‘A’: [1, 2, 3], ‘B’: [4, 5, 6]})

df2 = pd.DataFrame({‘A’: [10, 20, 30], ‘B’: [40, 50, 60]})

df3 = pd.DataFrame({‘A’: [100, 200, 300], ‘B’: [400, 500, 600]})

#appending data frames

new_df = pd.concat([df1, df2, df3], ignore_index=True)

#printing new dataframe

print(new_df)

“`

And the output will be:

“`

A B

0 1 4

1 2 5

2 3 6

3 10 40

4 20 50

5 30 60

6 100 400

7 200 500

8 300 600

“`

Additional Resources

Pandas has a wealth of functions, and beginners may find it hard to cover them all. Advanced users, on the other hand, may want to go deeper into Pandas capabilities.

Tutorials provide an opportunity to learn from examples and find out how other uses approach the use of Pandas. Below are some links to popular sites with tutorials and code examples:

1.

Pandas documentation – https://pandas.pydata.org/docs/

2. Real Python Pandas Tutorial – https://realpython.com/learning-paths/pandas-data-science/

3.

Kaggle Pandas Tutorial – https://www.kaggle.com/learn/pandas

4. DataCamp Pandas Tutorial- https://www.datacamp.com/projects/38

Conclusion

The Pandas library in Python provides a dynamic tool that helps data analysts and data scientists acquire insights into data. Combining data frames is one of the most common tasks performed when working with data.

This is where Pandas shines. We have shown how to append data frames using Pandas concat() function, and how to combine with more than two data frames.

With the examples provided and links to Panda tutorials and resources, new and seasoned Python developers can acquire more skills in Data processing. In summary, combining data frames using Pandas can provide valuable insights into data.

The process of appending Pandas data frames is simple, and it involves aligning columns and using the concat() function. This can be employed in combining more than two data frames, which allows for a more comprehensive analysis.

As one of the most critical tasks involved in data processing, knowing how to combine data frames is indispensable for any data analyst or scientist. Utilizing the available resources and tutorials makes it much easier to master this skill.

Overall, Pandas data frame combination is essential for streamlined and efficient data processing and analysis.

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