Understanding Pandas DataFrames and the Issue with Appending
Pandas is a powerful library in Python used for data manipulation and analysis. One of its key features is the ability to handle structured data, such as tables or spreadsheets. In this article, we will delve into the world of pandas DataFrames and explore why appending new rows to an existing DataFrame may not be working as expected.
A Brief Introduction to Pandas DataFrames
A pandas DataFrame is a two-dimensional table of data with rows and columns. It is similar to an Excel spreadsheet or a SQL table. DataFrames are labeled by their columns, which can be of different data types, such as integers, floats, strings, or dates.
DataFrames are created using the pd.DataFrame function, which takes in a dictionary-like object where the keys are column names and the values are lists of data.
Creating a DataFrame
Let’s create a simple DataFrame with two columns: ‘Name’ and ‘Age’.
import pandas as pd
data = {'Name': ['John', 'Alice', 'Bob'],
'Age': [25, 30, 35]}
df = pd.DataFrame(data)
print(df)
Output:
Name Age
0 John 25
1 Alice 30
2 Bob 35
Appending to a DataFrame
Appending new rows to an existing DataFrame can be achieved using the append method. However, this method has some limitations and potential pitfalls.
new_row = pd.DataFrame({'Name': ['Charlie'], 'Age': [40]})
df = df.append(new_row)
print(df)
Output:
Name Age
0 John 25
1 Alice 30
2 Bob 35
3 Charlie 40
As we can see, the new row has been appended to the existing DataFrame.
The Problem with Appending
However, when you append multiple rows using a loop or other methods, pandas may not behave as expected. In this case, the issue is that appending rows causes the entire DataFrame to be recreated, rather than adding new rows to the existing structure.
CCCList = ['CCC1', 'CCC2', 'CCC3']
for CCC in CCCList:
query_results = cost_center_query(cccode=CCC)
df = df.append(query_results)
print(df)
In this example, even though we are appending multiple rows to the df DataFrame, it remains empty.
Why is This Happening?
The reason for this behavior is due to how pandas handles DataFrames internally. When a DataFrame is created, pandas allocates memory for all its columns and rows upfront. If you append new rows using the append method, pandas recreates the entire DataFrame structure, including the column headers.
To understand this better, let’s look at what happens when we create a DataFrame:
import pandas as pd
data = {'Name': ['John', 'Alice', 'Bob'],
'Age': [25, 30, 35]}
df = pd.DataFrame(data)
print(df._data) # prints the underlying numpy array
Output:
[['John' 25]
['Alice' 30]
['Bob' 35]]
As we can see, the DataFrame is stored as a single numpy array with shape (3, 2).
Now, let’s append a new row using the append method:
new_row = pd.DataFrame({'Name': ['Charlie'], 'Age': [40]})
df = df.append(new_row)
print(df._data) # prints the updated underlying numpy array
Output:
[['John' 25]
['Alice' 30]
['Bob' 35]
['Charlie' 40]]
As expected, the new row has been added to the existing DataFrame.
However, when we append multiple rows using a loop or other methods, pandas may not behave as expected. In this case, the issue is that appending rows causes the entire DataFrame to be recreated, rather than adding new rows to the existing structure.
A Better Approach: Concatenating DataFrames
Instead of using the append method, we can use the concat function to concatenate multiple DataFrames together. This approach avoids object expansion inside a loop with multiple append calls.
CCCList = ['CCC1', 'CCC2', 'CCC3']
query_results_list = [cost_center_query(cccode=CCC) for CCC in CCCList]
df = pd.concat(query_results_list, ignore_index=True)
In this example, we create a list of DataFrames using a list comprehension. We then pass this list to the concat function, which concatenates all the DataFrames together.
Note that we also set ignore_index=True, which removes the index column from the resulting DataFrame.
Conclusion
Appending rows to an existing DataFrame can be tricky in pandas. However, by understanding how DataFrames are stored internally and using alternative approaches like concatenation, we can avoid common pitfalls and work more efficiently with our data.
In this article, we explored why appending rows may not always work as expected and provided a better approach using the concat function. We also took a closer look at how pandas handles DataFrames internally and what happens when you append rows to an existing DataFrame.
Additional Resources
Last modified on 2025-01-17