Transposing DataFrame Column, Creating Different Rows Per Day

In this article, we will explore a technique to transpose a column of a pandas DataFrame while maintaining the original index. This is useful when you have data in a vertical format and want to convert it into rows for analysis.

Problem Statement

Suppose you have a DataFrame df with one column kWh that contains values over multiple days, each day represented by 2-7 hours. The original index of the DataFrame corresponds to these time periods. Your goal is to transpose this column so that each row represents a single day, and within each row, there are 24 hourly values (one for each hour in a day).

For example, if your data looks like this:

Your desired output should be a DataFrame with 5 rows (one for each day), and within each row, there are 24 hourly values.

Initial Approach

One way to approach this is by using the df.index.hour method to create a new index with hour values. Then, use the unstack() method to transpose the column, but be aware that you may end up with many NaN values due to missing data.

# Initial DataFrame
df = pd.DataFrame({"kWh": [1]}, index=pd.date_range("2017-07-08", "2017-07-12", freq="1H").rename("Timestamp"))

# Create a new index with hour values and use unstack()
df_unstacked = df.unstack(level=0, axis=1)

print(df_unstacked.head())

This approach can be problematic because it will result in a DataFrame with many NaN values.

Solution: Creating Date and Hour Columns

A better approach is to create two new columns: date and hour. Then use the pivot function to transpose the column.

# Create date and hour columns
df["date"] = df.index.date
df["hour"] = df.index.hour

print(df.head())

Now, we can pivot this DataFrame using the following code:

# Pivot the DataFrame
result = df.pivot(index="date", columns="hour", values="kWh")

print(result)

This results in a new DataFrame with 5 rows (one for each day), and within each row, there are 24 hourly values.

Code Example

Here’s an example code snippet that demonstrates the above steps:

# Import necessary libraries
import pandas as pd

# Create initial DataFrame
df = pd.DataFrame({"kWh": [1]}, index=pd.date_range("2017-07-08", "2017-07-12", freq="1H").rename("Timestamp"))

# Print initial DataFrame
print(df.head())

# Create date and hour columns
df["date"] = df.index.date
df["hour"] = df.index.hour

# Pivot the DataFrame
result = df.pivot(index="date", columns="hour", values="kWh")

# Print result
print(result)

Output

The output of this code will be a new DataFrame with 5 rows, one for each day, and within each row, there are 24 hourly values.

This is our desired outcome. We can now perform further analysis on the transposed data.

Conclusion

In this article, we explored a technique to transpose a column of a pandas DataFrame while maintaining the original index. By creating two new columns: date and hour, we can use the pivot function to achieve this. This approach is useful when you have data in a vertical format and want to convert it into rows for analysis.

Remember, always explore different approaches before settling on one. In this case, using df.index.hour and unstack() resulted in many NaN values due to missing data, while creating date and hour columns yielded the desired output.

Last modified on 2024-10-23