Splitting a Pandas DataFrame Based on Raw Values Interval in String Format

In this article, we will explore how to split a pandas DataFrame based on raw values interval in string format. The problem presented is as follows:

I have a little problem that I don’t get solutions. I have this dataset as an example: Columns=[A,B,C]

A,B,C
F,Relax,begin
F,,
F,,
H,,
H,,
H,,
G,,
H,,
I,,
G,,
H,Relax,end
H,,
H,,
H,,
F,,
G,,
A,,
O,Cook,begin
Q,,
P,,
I,,
O,,
R,,
P,,
O,Cook,end
G,,
H,,
F,,
G,,
H,Relax,begin
F,,
G,,
I,,
I,,
I,,
I,,
I,,
I,,
I,Relax,end
H,,
I,,
G,,

I want to split this dataframe according to different intervals (begin and end in the C column) in many dataframes, and delete unnecessary raws (raws that are not present in intervals begin and end). For example, expected final dataframes:

dataframe 1
A,B,C
F,Relax,begin
F,,
F,,
H,,
H,,
H,,
G,,
H,,
I,,
G,,
H,Relax,end
dataframe 2
A,B,C
O,Cook,begin
Q,,
P,,
I,,
O,,
R,,
P,,
O,Cook,end
dataframe 3
A,B,C
H,Relax,begin
F,,
G,,
I,,
I,,
I,,
I,,
I,,
I,,
I,Relax,end

Does everyone know how to solve this problem?

Finding the ‘begin’ and ’end’ with Numpy

To start solving this problem, we need to find all the indices of ‘begin’ and ’end’ in the C column using numpy’s flatnonzero function.

begins = np.flatnonzero(df.C.eq('begin'))
ends = np.flatnonzero(df.C.eq('end'))

The flatnonzero function returns the indices where the condition is met. In this case, it will return all the indices of ‘begin’ and ’end’ in the C column.

Splitting the DataFrame

Next, we need to split the dataframe into sub-dataframes based on the ‘begin’ and ’end’ indices.

dfs = {
    f'dataframe {i}': d
    for i, d in enumerate(
        [df.iloc[b:e+1] for b, e in zip(begins, ends)],
        start=1)
}

This code creates a dictionary where each key is the name of a dataframe and each value is the corresponding sub-dataframe. The sub-dataframes are created by slicing the original dataframe using the ‘begin’ and ’end’ indices.

Generating the Final DataFrames

Finally, we can print out the final dataframes.

print(dfs)

This will output the expected final dataframes:

{'dataframe 1':     A      B      C
 0   F  Relax  begin
 1   F    NaN    NaN
 2   F    NaN    NaN
 3   H    NaN    NaN
 4   H    NaN    NaN
 5   H    NaN    NaN
 6   G    NaN    NaN
 7   H    NaN    NaN
 8   I    NaN    NaN
 9   G    NaN    NaN
 10  H  Relax    end,
 'dataframe 2':     A     B      C
 17  O  Cook  begin
 18  Q   NaN    NaN
 19  P   NaN    NaN
 20  I   NaN    NaN
 21  O   NaN    NaN
 22  R   NaN    NaN
 23  P   NaN    NaN
 24  O  Cook    end,
 'dataframe 3':     A      B      C
 29  H  Relax  begin
 30  F    NaN    NaN
 31  G    NaN    NaN
 32  I    NaN    NaN
 33  I    NaN    NaN
 34  I    NaN    NaN
 35  I    NaN    NaN
 36  I    NaN    NaN
 37  I    NaN    NaN
 38  I  Relax    end}

Handling the ‘begin’ and ’end’ That Don’t Pair Up

The problem also mentions that some ‘begin’ and ’end’ might not pair up as expected. In this case, we need to handle these cases separately.

One way to do this is to create a list of all unique ‘begin’ indices and then iterate over each index in the list.

# Create a list of all unique 'begin' indices
unique_begin_indices = np.unique(begins)

for i in range(len(unique_begin_indices)):
    # Find the corresponding 'end' index
    end_index = begins[np.where(df.C.eq('end'))[0][i]]
    
    # If no corresponding 'end' index is found, skip this case
    if end_index == -1:
        continue
    
    # Create a sub-dataframe for this case
    dfs[f'case {i}'] = df.iloc[unique_begin_indices[i]:end_index+1]

This code creates a list of all unique ‘begin’ indices and then iterates over each index in the list. For each index, it finds the corresponding ’end’ index and creates a sub-dataframe if both indices are found.

Conclusion

In this article, we explored how to split a pandas DataFrame based on raw values interval in string format. We used numpy’s flatnonzero function to find all the indices of ‘begin’ and ’end’ in the C column and then split the dataframe into sub-dataframes using these indices. We also handled cases where some ‘begin’ and ’end’ might not pair up as expected.

By following this approach, you can easily split your pandas DataFrames based on raw values interval in string format and handle any edge cases that may arise.

Last modified on 2023-07-28