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Pandas Cheatsheet (Avoid indexes)
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| import pandas as pd | |
| ### Data import ### | |
| # https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_csv.html | |
| df = pd.read_csv("file.csv") | |
| ### Data types ### | |
| df.dtypes | |
| ### DataFrame operations ### | |
| # Subset, rename, retype columns | |
| df[["a", "b"]] | |
| df.rename(columns={"y":"year"}) | |
| df.astype({"a": int}) | |
| # Unique and duplicated values | |
| df["col"].unique # numpy array of unique values | |
| df["col"].is_unique | |
| df.drop_duplicates(subset=["col"]) | |
| df.duplicated() # Boolean Series | |
| # Summary statistics | |
| df["col"].value_counts(dropna=False) | |
| df["col"].isna().sum() | |
| # Table join | |
| # Warning: NULLs are joined together, unlike SQL | |
| # https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.merge.html | |
| # Use on="key" if joining columns are named the same | |
| df.merge(df2, how="left", left_on="lk", right_on="rk") | |
| # SQL-style GROUP BY named aggregation functions | |
| # Common functions: size, count (non-NAs), nunique (count distinct), first, max, min, mean | |
| df.groupby("group", as_index=False).agg( | |
| a_sum=('a', "sum"), | |
| d_range=('d', lambda x: x.max() - x.min()) | |
| ) | |
| ### Data export | |
| df.to_csv("file.csv", index=False) |
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