Sourcery Starbot ⭐ refactored prakharrathi25/data-storyteller #14
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SourceryAI
commented
Jun 17, 2022
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Found the following improvement in Function app:
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Function app refactored with the following changes:
- Use f-string instead of string concatenation [×2] (
use-fstring-for-concatenation)
| # Load the data | ||
| # Load the data |
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Function app refactored with the following changes:
- Merge append into list declaration [×2] (
merge-list-append) - Move assignment closer to its usage within a block [×2] (
move-assign-in-block)
This removes the following comments ( why? ):
# Table to store model and accurcy
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| # Use two column technique | ||
| col1, col2 = st.beta_columns(2) | ||
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| global name, type | ||
| # Design column 1 | ||
| name = col1.selectbox("Select Column", data.columns) | ||
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| # Design column two | ||
| current_type = col_metadata[col_metadata['column_name'] == name]['type'].values[0] | ||
| print(current_type) | ||
| column_options = ['numeric', 'categorical','object'] | ||
| current_index = column_options.index(current_type) | ||
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| type = col2.selectbox("Select Column Type", options=column_options, index = current_index) | ||
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| st.write("""Select your column name and the new type from the data. | ||
| To submit all the changes, click on *Submit changes* """) | ||
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| if st.button("Change Column Type"): | ||
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| # Set the value in the metadata and resave the file | ||
| # col_metadata = pd.read_csv('data/metadata/column_type_desc.csv') | ||
| st.dataframe(col_metadata[col_metadata['column_name'] == name]) | ||
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Found the following improvement in Function app:
| if 'models' in os.listdir('../'): | ||
| return True | ||
| return False | ||
| return 'models' in os.listdir('../') |
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Function checkDir refactored with the following changes:
- Simplify boolean if expression (
boolean-if-exp-identity) - Remove unnecessary casts to int, str, float or bool (
remove-unnecessary-cast) - Lift code into else after jump in control flow (
reintroduce-else) - Replace if statement with if expression (
assign-if-exp)
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Found the following improvement in Function app:
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| elif is_numeric_dtype(df[col[i]]): | ||
| ColumnType.append((col[i],"numerical")) | ||
| Numerical.append(col[i]) | ||
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Found the following improvement in Function genMetaData:
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| uniqueVals = list(np.unique(col)) | ||
| uniqueVals.sort() | ||
| dict_ = {uniqueVals[i]: i for i in range(len(uniqueVals))} | ||
| return dict_ | ||
| uniqueVals = sorted(np.unique(col)) | ||
| return {uniqueVals[i]: i for i in range(len(uniqueVals))} |
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Function makeMapDict refactored with the following changes:
- Remove an unnecessary list construction call prior to sorting (
skip-sorted-list-construction) - Inline variable that is immediately returned (
inline-immediately-returned-variable)
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| redunt = [] | ||
| k = 0 | ||
| for ind, c in enumerate(corr[y]): | ||
| if c<1-threshold: | ||
| redunt.append(cols[ind]) | ||
| return redunt | ||
| return [cols[ind] for ind, c in enumerate(corr[y]) if c<1-threshold] |
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Function getRedundentColumns refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable) - Move assignment closer to its usage within a block (
move-assign-in-block) - Convert for loop into list comprehension (
list-comprehension)
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| newDF = df.drop(columns2Drop, axis = 'columns') | ||
| return newDF | ||
| return df.drop(columns2Drop, axis = 'columns') |
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Function newDF refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
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