Query: performancewarning: dataframe is highly fragmented. this is usually the result of calling `frame.insert` many times, which has poor performance. consider joining all columns at once using pd.concat(axis=1) instead. to get a de-fragmented frame, use `newframe = frame.copy()`
Explanation: When working with a DataFrame in pandas, it is important to be aware of the performance implications of certain operations. One scenario where performance can be affected is when calling the `frame.insert` method multiple times. This can lead to a highly fragmented DataFrame, which may have poor performance.
To address this issue, it is recommended to use the `pd.concat(axis=1)` method to join all columns at once. This approach results in a more efficient operation compared to calling `frame.insert` multiple times.
In case you already have a highly fragmented frame, you can create a de-fragmented copy of the frame using the `newframe = frame.copy()` statement. This will give you a new DataFrame with the same data but without the fragmentation issues.
Let’s consider an example to illustrate this:
<pre><code>import pandas as pd
# Creating a DataFrame with two columns
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
# Fragmenting the DataFrame using frame.insert method
df.insert(1, 'C', [7, 8, 9]) # Inserting column C
df.insert(2, 'D', [10, 11, 12]) # Inserting column D
# Checking the fragmented DataFrame
print(df)
# Output:
# A C D B
# 0 1 7 10 4
# 1 2 8 11 5
# 2 3 9 12 6
# Joining all columns at once using pd.concat(axis=1)
df_concatenated = pd.concat([df['A'], df['C'], df['D'], df['B']], axis=1)
# Checking the de-fragmented DataFrame
print(df_concatenated)
# Output:
# A C D B
# 0 1 7 10 4
# 1 2 8 11 5
# 2 3 9 12 6
# Creating a de-fragmented copy of the DataFrame
new_frame = df.copy()
# Checking the de-fragmented copy
print(new_frame)
# Output:
# A C D B
# 0 1 7 10 4
# 1 2 8 11 5
# 2 3 9 12 6</code></pre>In this example, we start with a DataFrame `df` containing two columns ‘A’ and ‘B’. We then use the `frame.insert` method to insert two additional columns ‘C’ and ‘D’, resulting in a fragmented DataFrame. We print the fragmented DataFrame and observe the order of the columns.
Next, we address the fragmentation issue by using `pd.concat(axis=1)` to join all columns at once. We create a new DataFrame `df_concatenated` by passing a list of the column names and specifying `axis=1` to concatenate columns horizontally. We print the de-fragmented DataFrame and notice that the order of the columns is now fixed.
Finally, we demonstrate the creation of a de-fragmented copy of the DataFrame using the `frame.copy()` method. We assign the copied DataFrame to a new variable `new_frame` and print it to verify that it does not have any fragmentation issues.