So I gave your case a try, even though I don’t quite understand your case. Based on my results below, I don’t think it is lazy. On top of @Diet 's suggestion, I want to add that it’s still preferred to let the model work block by block. Knowing what context is necessary for the model to work is something we human have to deal with at this point.
My prompt:
I have a code snippet and I need to you to improve it so that it's not fragmented in memory.
Based on my understanding it could be due to calling `insert` many times in the program.
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Response = "To address the performance warning and avoid creating a highly fragmented DataFrame, you can refactor the code to perform operations on the entire DataFrame or subsets of it without using chained indexing inside a loop. Instead of inserting columns one by one, you can calculate all the necessary values and then concatenate them to the original DataFrame using `pd.concat`.
Here's a refactored version of your code:
```python
import pandas as pd
import numpy as np
from scipy.stats import gamma
# Assuming inspection_eff_mapping, CF_pass_mapping, CF_fail_mapping, and mod_scale_param_for_calc are defined elsewhere
# Create empty dictionaries to store the new columns
new_columns = {}
# Loop through each test history for rows where 'jumlah_historis_pengujian' is non-zero
max_value = df.loc[mask_test_history_non_zero, 'jumlah_historis_pengujian'].max()
for i in range(1, max_value + 1):
# Calculate the gap in years for the current test history to previous inspection
if i == 1:
new_columns[f'test_date_gap_years_{i}'] = (df[f'tanggal_pengujian_{i}'] - df['tanggal_mulai_operasi']).dt.days / 365.25
else:
new_columns[f'test_date_gap_years_{i}'] = (df[f'tanggal_pengujian_{i}'] - df[f'tanggal_pengujian_{i - 1}']).dt.days / 365.25
# Other calculations similar to the above, for example:
# new_columns[f'p_prd_f_prior_{i}'] = 1 - np.exp(-np.power(new_columns[f'test_date_gap_years_{i}'] / df['mod_scale_param'], df['def_shape_param']))
# ... (include all other calculations and column assignments here)
# After all calculations, concatenate the new columns to the original DataFrame
df = pd.concat([df, pd.DataFrame(new_columns)], axis=1)
# Now you can perform the rest of the operations that depend on the new columns
# For example, setting 'tanggal_assessment' and other columns that depend on the newly created columns
df['tanggal_assessment'] = df[[f'tanggal_rev_{i-1}' for i in range(1, max_value + 1)]].bfill(axis=1).iloc[:, 0]
# ... (include all other operations that depend on the new columns here)
# Finally, if you need to update the original DataFrame with the new values, you can do so
# df.update(new_df)
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At ChatGPT
Your approach to improving memory fragmentation in your code is quite sensible. The original implementation, which involves inserting columns one by one into a DataFrame, can indeed lead to memory fragmentation, especially if the DataFrame is large. Your new approach of computing all the necessary values first and then concatenating them to the original DataFrame is more efficient.
However, there are additional optimizations we can consider:
1. **Pre-allocation of DataFrame**: Instead of using dictionaries to store new columns and then creating a DataFrame, you can pre-allocate a DataFrame with the required size and data types. This approach minimizes memory reallocation.
2. **Vectorized Operations**: Ensure that all calculations within the loop are vectorized. Avoid using explicit Python loops for operations that can be vectorized. This won't directly affect memory fragmentation but will significantly improve performance.
3. **Minimize Data Copying**: Each operation in pandas that alters a DataFrame tends to create a copy. Try to minimize these operations. For example, rather than concatenating new columns, consider initializing the DataFrame with all necessary columns and then populating it.
Here's how you can refactor the code with these considerations:
pythonCopy code
```
import pandas as pd
import numpy as np
from scipy.stats import gamma
# Assumptions: inspection_eff_mapping, CF_pass_mapping, CF_fail_mapping, and mod_scale_param_for_calc are defined elsewhere
# Assumption: df is your original DataFrame
max_value = df.loc[mask_test_history_non_zero, 'jumlah_historis_pengujian'].max()
# Pre-allocate a DataFrame for the new columns
new_cols_data = {f'test_date_gap_years_{i}': np.zeros(len(df)) for i in range(1, max_value + 1)}
new_df = pd.DataFrame(new_cols_data)
for i in range(1, max_value + 1):
if i == 1:
new_df[f'test_date_gap_years_{i}'] = (df[f'tanggal_pengujian_{i}'] - df['tanggal_mulai_operasi']).dt.days / 365.25
else:
new_df[f'test_date_gap_years_{i}'] = (df[f'tanggal_pengujian_{i}'] - df[f'tanggal_pengujian_{i - 1}']).dt.days / 365.25
# Perform other calculations and populate new_df accordingly
# Concatenate the new DataFrame with the original one
df = pd.concat([df, new_df], axis=1)
# Perform subsequent operations on df
```
In this revised version:
* `new_df` is pre-allocated with all the new columns, which is more memory efficient.
* Calculations within the loop should be as vectorized as possible.
* By directly concatenating `new_df` with `df`, we minimize additional data copying.
Remember, the specifics of optimization may vary depending on the size of your DataFrame and the nature of your calculations, so it's always good to profile your code to identify bottlenecks.
With API GPT-4-turbo
I do add some system prompt to restrict the model from explaining the code, but I don’t have access to the system prompt at this point.
import pandas as pd
import numpy as np
# Assuming df is the original DataFrame and mask_test_history_non_zero is already defined
max_value = df.loc[mask_test_history_non_zero, 'jumlah_historis_pengujian'].max()
new_data = {f'test_date_gap_years_{i}': [] for i in range(1, max_value + 1)}
for index, row in df.iterrows():
for i in range(1, max_value + 1):
if i == 1:
gap_years = (row[f'tanggal_pengujian_{i}'] - row['tanggal_mulai_operasi']).days / 365.25
else:
gap_years = (row[f'tanggal_pengujian_{i}'] - row[f'tanggal_pengujian_{i - 1}']).days / 365.25
new_data[f'test_date_gap_years_{i}'].append(gap_years)
# Convert the dictionary to a DataFrame
new_columns_df = pd.DataFrame(new_data)
# Concatenate the new columns to the original DataFrame
df = pd.concat([df, new_columns_df], axis=1)
