Appending DataFrames in Pandas: A Tutorial

Understanding the Append function in Pandas

Pandas is a popular library in Python that provides data structures and functions for the manipulation and analysis of numerical tables and time series. Append function is used to append a DataFrame to another data frame. Here’s an in-depth guide on how to make use of Pandas Append function to combine multiple DataFrames.

How Append Function Works?

Pandas Append method concatenates two DataFrames vertically. There are two ways to use append function:

• df.append(df2)
• pd.concat([df1,df2])

df.append method takes one argument which is the DataFrame to append, whereas pd.concat can take multiple DataFrames as arguments in a list.

Example:

#Create two sample dataframes
df1 = pd.DataFrame({'A':[1,2], 'B':[3,4]})
df2 = pd.DataFrame({'A':[5,6], 'B':[7,8]})

#concatenate dataframes
result = pd.concat( [ df1, df2 ] )

In this example, df1 and df2 are concatenated by using the pd.concat() function, with the result stored in a new variable called result.

Combining DataFrames with Append

Let’s take a look at an example for using Append with the parameter ignore_index.

#Create two sample dataframes
df1 = pd.DataFrame({'A':[1,2], 'B':[3,4]})
df2 = pd.DataFrame({'A':[5,6], 'B':[7,8]})

#Using Append function
result = df1.append(df2, ignore_index=True)

In this example, the ignore_index parameter is set to True. When set to True, the index is reset to start from zero.

Handling Duplicated Rows

append() function stacks two DataFrames one underneath the other. By default, duplicated rows are not removed. If required, you can remove duplicates by applying the drop_duplicates() function.

Example:

#Create two sample dataframes
df1 = pd.DataFrame({'A':[1,2], 'B':[3,4]})
df2 = pd.DataFrame({'A':[2,3,3], 'B':[3,7,8]})

#Appending DataFrames with Duplicates
result = df1.append(df2)
print(result)

#Removing Duplicates
result_no_duplicates = result.drop_duplicates()
print(result_no_duplicates)

In this example, the DataFrames are concatenated with the same duplicate row. The drop_duplicates() function is used to remove duplicate rows resulting in a DataFrame with unique rows.

That’s it! You now have an understanding of how to use Pandas Append function in Python. Data handling with these tools is extremely useful for any data scientist, so it’s always good to practice your skills in this area.

Appending DataFrames with matching columns

Pandas Append function can be used to append two DataFrames with matching columns into a single DataFrame. In this section, we will discuss how to append two DataFrames with matching columns and what happens when there are missing values.

When appending DataFrames with matching columns, the columns will be concatenated. The rows of both DataFrames will be stacked one underneath the other.

Example:

#Create two sample dataframes
df1 = pd.DataFrame({'A':[1,2], 'B':[3,4]})
df2 = pd.DataFrame({'A':[5,6], 'B':[7,8]})

#Appending 2 DataFrames with Matching Columns
result = df1.append(df2)
print(result)

In this example, df1 and df2 are appended simply by using the append function. The resulting DataFrame has both DataFrames’ rows stacked one after the other.

Handling Missing Values

When appending DataFrames with matching columns, it’s important to consider missing values. By default, if a column has a mismatch in the number of rows between the two DataFrames, undefined (NaN) value is added for the corresponding row of the smaller DataFrame.

Example:

#Create two sample dataframes
df1 = pd.DataFrame({'A':[1,2], 'B':[3,4]})
df2 = pd.DataFrame({'A':[5,6], 'B':[7,8], 'C':[9,10]})

#Appending 2 DataFrames with Missing columns
result = df1.append(df2)
print(result)

In this example, df2 has an additional column named C. The resulting DataFrame has undefined value (NaN) for the corresponding C column of the smaller DataFrame.

It’s important to account for and fill the undefined values, as NaN values can skew data analysis results such as minimum, maximum, and mean values.

Conclusion

In conclusion, concatenating or appending DataFrames is a common data cleaning task that data scientists need to do. You can use Pandas’ Append function for this task as it provides a fast, efficient, and powerful way to merge or combine DataFrames. Make sure to pay attention to column matching and missing values to avoid unexpected results.

Combining DataFrames with non-matching columns

Combining DataFrames with non-matching columns is a common task when working with large datasets. Pandas provides several ways to combine DataFrames based on the common columns in a number of ways. In this section, we’ll discuss how to combine non-matching DataFrames using merge() function.

Using merge() Function

Pandas’ merge() function allows the user to combine DataFrames based on the common columns. It has two primary parameters; left and right. In merge() function, the left and right parameters represent the two DataFrames that you want to join or combine.

Example:

# Create two sample dataframes
df1 = pd.DataFrame({'A':['A0','A1','A2'], 'B':['B0','B1','B2'], 'C':['C0','C1','C2'], 'D':['D0','D1','D2']})
df2 = pd.DataFrame({'A':['A3','A4','A5'], 'B':['B3','B4','B5'], 'C':['C3','C4','C5'], 'D':['D3','D4','D5']})

# Combine two DataFrames using merge() function
result = pd.merge(left=df1, right=df2, on=['A', 'B'])
print(result)

In this example, we have two DataFrames (df1 and df2) with non-matching columns. The merge() function is used to merge the DataFrames df1 and df2 on the common columns A and B.

Types of Merge in merge() Function

There are several types of merge you can use in merge() function:

• Inner Merge: Merge where only the common intersecting values are merged.
• Outer Merge: Merge where all values are merged, even if they don’t have a matching value.
• Left Merge: Merge where only values from the left DataFrame are merged.
• Right Merge: Merge where only values from the right DataFrame are merged.

Example:

# Create two sample dataframes
df1 = pd.DataFrame({'A':['A0','A1','A2'], 'B':['B0','B1','B2'], 'C':['C0','C1','C2'], 'D':['D0','D1','D2']})
df2 = pd.DataFrame({'A':['A3','A4','A5'], 'B':['B3','B4','B5'], 'E':['C3','C4','C5'], 'F':['D3','D4','D5']})

# Outer Merge
result_outer = pd.merge(left=df1, right=df2, on=['A', 'B'], how='outer')
print(result_outer)

# Left Merge
result_left = pd.merge(left=df1, right=df2, on=['A', 'B'], how='left')
print(result_left)

In this example, we have used the merge() function to perform outer and left merge on two DataFrames.

Conclusion

In conclusion, Pandas provides a convenient merging function called merge() to combine DataFrames with non-matching columns. Make sure to properly define the merge keys and select the appropriate type of merge to avoid skewing the results of your data analysis.

Summary

Pandas’ Append function is a useful tool to merge or combine DataFrames. Whether you have matching or non-matching columns, Append offers different methods to concatenate DataFrames efficiently. When using Append, pay attention to handle duplicates, undefined values, and data types to avoid errors in result. In my experience, Pandas’ Append function has been a go-to tool to combine datasets during data-cleaning and data wrangling steps. It’s easy to handle and saves time in merging large datasets on the go!