本文簡要介紹
pyspark.pandas.DataFrame.align
的用法。用法:
DataFrame.align(other: Union[DataFrame, Series], join: str = 'outer', axis: Union[int, str, None] = None, copy: bool = True) → Tuple[DataFrame, Union[DataFrame, Series]]
使用指定的連接方法將兩個對象在其軸上對齊。
為每個軸索引指定連接方法。
- other:DataFrame 或係列
- join:{{‘outer’, ‘inner’, ‘left’, ‘right’}},默認 ‘outer’
- axis:其他對象的允許軸,默認無
對齊索引 (0)、列 (1) 或兩者(無)。
- copy:布爾值,默認為真
總是返回新對象。如果 copy=False 並且不需要重新索引,則返回原始對象。
- (left, right):(DataFrame,其他類型)
對齊的對象。
參數:
返回:
例子:
>>> ps.set_option("compute.ops_on_diff_frames", True) >>> df1 = ps.DataFrame({"a": [1, 2, 3], "b": ["a", "b", "c"]}, index=[10, 20, 30]) >>> df2 = ps.DataFrame({"a": [4, 5, 6], "c": ["d", "e", "f"]}, index=[10, 11, 12])
對齊兩個軸:
>>> aligned_l, aligned_r = df1.align(df2) >>> aligned_l.sort_index() a b c 10 1.0 a NaN 11 NaN None NaN 12 NaN None NaN 20 2.0 b NaN 30 3.0 c NaN >>> aligned_r.sort_index() a b c 10 4.0 NaN d 11 5.0 NaN e 12 6.0 NaN f 20 NaN NaN None 30 NaN NaN None
僅對齊軸 = 0(索引):
>>> aligned_l, aligned_r = df1.align(df2, axis=0) >>> aligned_l.sort_index() a b 10 1.0 a 11 NaN None 12 NaN None 20 2.0 b 30 3.0 c >>> aligned_r.sort_index() a c 10 4.0 d 11 5.0 e 12 6.0 f 20 NaN None 30 NaN None
僅對齊軸 = 1(列):
>>> aligned_l, aligned_r = df1.align(df2, axis=1) >>> aligned_l.sort_index() a b c 10 1 a NaN 20 2 b NaN 30 3 c NaN >>> aligned_r.sort_index() a b c 10 4 NaN d 11 5 NaN e 12 6 NaN f
與連接類型 “inner” 對齊:
>>> aligned_l, aligned_r = df1.align(df2, join="inner") >>> aligned_l.sort_index() a 10 1 >>> aligned_r.sort_index() a 10 4
與係列對齊:
>>> s = ps.Series([7, 8, 9], index=[10, 11, 12]) >>> aligned_l, aligned_r = df1.align(s, axis=0) >>> aligned_l.sort_index() a b 10 1.0 a 11 NaN None 12 NaN None 20 2.0 b 30 3.0 c >>> aligned_r.sort_index() 10 7.0 11 8.0 12 9.0 20 NaN 30 NaN dtype: float64
>>> ps.reset_option("compute.ops_on_diff_frames")
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注:本文由純淨天空篩選整理自spark.apache.org大神的英文原創作品 pyspark.pandas.DataFrame.align。非經特殊聲明,原始代碼版權歸原作者所有,本譯文未經允許或授權,請勿轉載或複製。