10 minutes to pandas — pandas 2.1.1 documentation --- 10 分钟了解 pandas — pandas 2.1.1 文档

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• Creating a Series by passing a list of values, letting pandas create a default integer index:

• Creating a DataFrame by passing a NumPy array, with a datetime index using date_range() and labeled columns:

• Creating a DataFrame by passing a dictionary of objects that can be converted into a series-like structure:

• df.sort_values(by="B")

• s, DataFrame.at(), DataFrame.iat()

• Selecting a single column, which yields a Series,

• Selecting on a multi-axis by label: >>> In [27]: df.loc[:, ["A", "B"]]

• By lists of integer position locations, similar to the NumPy/Python style: >>> In [34]: df.iloc[[1, 2, 4], [0, 2]]

• df2[df2["E"].isin(["two", "four"])]

• Using the isin() method for filtering:

• df.at[dates[0], "A"] = 0

• df2[df2 > 0] = -df2

• A where operation with setting:

• Reindexing allows you to change/add/delete the index on a specified axis. This returns a copy of the data:

• f1 = df.reindex(index=dates[0:4], columns=list(df.columns) + ["E"])

• ).shift(2)

• df.sub(s, axis="index")

• s.value_counts()

• # break it into pieces In [75]: pieces = [df[:3], df[3:7], df[7:]] In [76]: pd.concat(pieces) Out[76]:

• df.groupby("A")[["C", "D"]].sum()