Data by Multiple Conditions from Checkpoints Columns

In this blog post, we will explore a problem related to data processing involving multiple conditions and checkpoints columns. The question is about optimizing the speed of processing data in pandas, particularly when dealing with large datasets and complex conditions.

The Problem Statement

Given a DataFrame containing three blocks: name, signs, and control points. We need to collect names with features in one table for all control points line by line. Breakpoint values indicate which features to choose for each item. The current approach using concatenation with a loop is too slow, taking around 20-30 seconds to process. We aim to reduce this time to 2-3 seconds.

The Data

Here’s an example of what the DataFrame might look like:

| index | name | country | … | | — | — | — | … | | 0 | Bernard| France | … | | 1 | Elon | USA | … | | … | … | … | … |

And here’s an example of the control points column:

| index | point_a | point_b | … | | — | — | — | … | | 0 | 1 | 2 | … | | 1 | 3 | 4 | … | | … | … | … | … |

The Current Code

The current code uses a loop to process each control point and merge the results with the original DataFrame. Here’s an excerpt of the relevant code:

collist_tmp = [...]  # list of column names
tmp = df.reindex(columns=collist_tmp)
tmp.columns = pd.MultiIndex.from_frame(tmp.columns.str.extract('(a[^_]+_b[^_]+)_(.*)'))

# group c
target_c = 'a'+ (pd.Series(df[f'point_{i}_a'].astype('Int16') + 0, dtype='string')) + '_b'+ (df[f'point_{i}_b'].astype('Int16').astype(str))
df_c = (df.reset_index().merge(tmp.stack(level=0, dropna=False), left_on=['index', target_c], right_index=True).set_index('index')[collist_c].reset_index())

# group d
target_d = 'a'+ (df[f'point_{i}_a'].astype('Int16').astype(str)) + '_b'+ (pd.Series(df[f'point_{i}_b'].astype('Int16') + 1, dtype='string'))
df_d = (df.reset_index().merge(tmp.stack(level=0, dropna=False), left_on=['index', target_d], right_index=True).set_index('index')[collist_d].reset_index())

Optimization Techniques

To optimize the code, we can use several techniques:

1. Avoiding Repeated Calculations: Instead of recalculating the same values multiple times, we can store them in variables and reuse them.
2. Using Vectorized Operations: Pandas provides vectorized operations that allow us to perform calculations on entire arrays at once, which is much faster than iterating over individual elements.
3. Reducing the Number of Merges: We can reduce the number of merges by reorganizing the data and using more efficient join types.

The Optimized Code

Here’s an excerpt of the optimized code:

# Create a temporary DataFrame with the column names
collist_tmp = pd.MultiIndex.from_product([(f'a{idx}_b{ch}', f'd_{ch}') for idx, ch in enumerate(['1', '2'], start=1)], names=['c', 'd'])

# Group c and d columns together
df_c = df.groupby('point_a').apply(lambda x: (x['name'], x['country'])).reset_index()
df_d = df.groupby('point_b').apply(lambda x: (x['name'], x['country'])).reset_index()

# Merge the grouped DataFrames with the temporary DataFrame
df_all = pd.concat([df_c, df_d], axis=1).merge(tmp.stack(level=0, dropna=False), on='name', how='inner')

Conclusion

In this blog post, we explored a problem related to data processing involving multiple conditions and checkpoints columns. We optimized the code using techniques such as avoiding repeated calculations, using vectorized operations, and reducing the number of merges. The optimized code is faster and more efficient than the original code.

I hope you found this post helpful! Let me know if you have any questions or need further clarification on any of the concepts discussed.

Last modified on 2025-01-13