itsamejms
66 lines
1.8 KiB
Python
66 lines
1.8 KiB
Python
# %%
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import pyreadstat
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import json
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import re
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import datetime
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# %%
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df_sav, meta = pyreadstat.read_sav('./data/ZA8841_v1-0-0.sav')
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print(df_sav.shape)
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# %%
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df_sav.head(1).to_dict(orient='records')
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# %%
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def safe_json(obj):
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if isinstance(obj, (datetime.datetime, datetime.date)):
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return obj.isoformat()
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if isinstance(obj, set):
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return list(obj)
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if hasattr(obj, '__dict__'):
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return str(obj)
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return obj
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meta_dict = vars(meta)
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meta_json = json.dumps(meta_dict, default=safe_json, indent=2)
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# %%
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with open('./data/za8841_meta.json', 'w') as f:
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f.write(meta_json)
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# %%
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# Load your mapping dictionary (from JSON file or directly)
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with open("./data/za8841_meta.json") as f:
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meta = json.load(f)
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value_labels = meta["variable_value_labels"]
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# %%
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# Assume df_sav is your loaded SPSS dataframe
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# For each column in the mapping, map values if the column exists in df_sav
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for col, mapping in value_labels.items():
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if col in df_sav.columns:
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# Convert keys to float if needed (SPSS values often are float)
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mapping_float = {float(k): v for k, v in mapping.items()}
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df_sav[col] = df_sav[col].map(mapping_float).fillna(df_sav[col])
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# Now all mapped columns have human-readable values
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print(df_sav.head())
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# %%
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labels_map = meta["column_names_to_labels"]
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def make_pandas_friendly(col):
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col = labels_map.get(col, col)
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col = re.sub(r'[.\s]+', '_', col)
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col = re.sub(r'[^0-9a-zA-Z_]', '', col)
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col = col.lower()
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col = re.sub(r'__+', '_', col) # Replace double (or more) underscores with single
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col = col.strip('_') # Remove leading/trailing underscores
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return col
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df_sav.columns = [make_pandas_friendly(col) for col in df_sav.columns]
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# %%
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df_sav.head(1).to_dict(orient='records')
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# %%
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