Sorting Multilevel Columns with Mixed Datatypes in Pandas
Introduction
Pandas is a powerful library used for data manipulation and analysis. It provides an efficient way to handle structured data, including tabular data such as spreadsheets and SQL tables. One of the common tasks when working with multilevel columns in pandas is sorting these columns based on different criteria while handling mixed datatypes.
In this article, we will discuss a specific scenario where we need to sort a multilevel column ('D', 'E') with mixed datatypes (integers, strings, empty dictionaries, and NaN) in descending order while preserving the rows that contain the substring 'all' in all earlier columns. We will explore various approaches to achieve this goal.
Finding Rows with Substring 'all'
The first step is to identify rows that contain the substring 'all' in all earlier columns before column ('D', 'E'). This can be achieved by applying a function to each row that checks if any of the strings in the previous columns contains 'all'.
mask = df.iloc[:, :df.columns.get_loc(('D','E'))].apply(lambda x: x.astype(str).str.contains('all').any(axis=1))
This mask will be used to filter out rows that do not contain the substring 'all' in all earlier columns.
Filtering Rows and Sorting
Next, we need to sort the remaining rows based on the values in column ('D', 'E'). We can use the pd.to_numeric function with errors=‘coerce’ to handle mixed datatypes. Then, we can use the sort_values method with ascending=False to sort the rows in descending order.
df1 = df[~mask].copy()
df1['tmp'] = pd.to_numeric(df1[('D','E')].str.get('value'), errors='coerce')
idx = df1.sort_values('tmp', ascending=False).index
Mapping Original Index Values
After sorting, we need to map the original index values to their corresponding sorted indices. This can be done using a dictionary.
d = dict(zip(df.index[~mask], idx))
Renaming Index and Sorting
Finally, we can rename the index of the original dataframe df using the mapped index values, sort the dataframe by its new index, and preserve the rows that contain the substring 'all'.
df = df.set_index(df.rename(d).index).sort_index()
Example Walkthrough
Let’s take a closer look at an example to understand this process better.
Suppose we have the following dataframe df:
| A | D | E |
|---|---|---|
| a | {‘value’: ‘126’, ‘perc’: None, ‘unit’: None} | {‘value’: 324, ‘perc’: None, ‘unit’: None} |
| b:all:c | {‘value’: 123, ‘perc’: None, ‘unit’: None} | {‘value’: 456, ‘perc’: None, ‘unit’: None} |
| all:1:3 | {‘value’: 789, ‘perc’: None, ‘unit’: None} | {‘value’: 1011, ‘perc’: None, ‘unit’: None} |
| d | {‘value’: 222, ‘perc’: None, ‘unit’: None} | {‘value’: 333, ‘perc’: None, ‘unit’: None} |
To sort this dataframe based on the values in column ('D', 'E'), we need to filter out rows that contain the substring 'all' in all earlier columns. The mask will be:
| A | D | E |
|---|---|---|
| b:all:c | {‘value’: 123, ‘perc’: None, ‘unit’: None} | {‘value’: 456, ‘perc’: None, ‘unit’: None} |
Next, we sort the remaining rows based on the values in column ('D', 'E'):
| A | D | E |
|---|---|---|
| b:all:c | {‘value’: 123, ‘perc’: None, ‘unit’: None} | {‘value’: 456, ‘perc’: None, ‘unit’: None} |
| all:1:3 | {‘value’: 789, ‘perc’: None, ‘unit’: None} | {‘value’: 1011, ‘perc’: None, ‘unit’: None} |
We then map the original index values to their corresponding sorted indices:
| A | D | E |
|---|---|---|
| b:all:c | {‘value’: 123, ‘perc’: None, ‘unit’: None} | {‘value’: 456, ‘perc’: None, ‘unit’: None} |
| all:1:3 | {‘value’: 789, ‘perc’: None, ‘unit’: None} | {‘value’: 1011, ‘perc’: None, ‘unit’: None} |
Finally, we rename the index of the original dataframe df using the mapped index values and sort it by its new index:
| A | D | E |
|---|---|---|
| a | {‘value’: ‘126’, ‘perc’: None, ‘unit’: None} | {‘value’: 324, ‘perc’: None, ‘unit’: None} |
| d | {‘value’: 222, ‘perc’: None, ‘unit’: None} | {‘value’: 333, ‘perc’: None, ‘unit’: None} |
Conclusion
Sorting multilevel columns with mixed datatypes in pandas requires a careful approach to handle the different types of data. By first filtering out rows that contain the substring 'all' in all earlier columns, then sorting the remaining rows based on the values in column ('D', 'E'), and finally mapping the original index values to their corresponding sorted indices, we can achieve our goal.
This approach ensures that the rows are sorted correctly while preserving the integrity of the data.
Last modified on 2025-04-03