本文简要介绍 pyspark.pandas.Series.drop_duplicates 的用法。用法:Series.drop_duplicates(keep: str = 'first', inplace: bool = False)→ Optional[pyspark.pandas.series.Series]返回删除重复值的系列。参数: keep:{‘first’, ‘last’, False },默认 ‘first’ 处理删除重复项的方法: - ‘first’ :...
drop_duplicates(keep='first', inplace=False) Parameters of the Series.drop_duplicates() Following are the parameters of the Series.drop_duplicates() function. keep –{‘first’, ‘last’, False}, default ‘first’ Determines which duplicates (if any) to keep. first –Keep the first ...
—drop_duplicates()，它可以帮助我们轻松地处理数据中的重复值。本文将详细介绍drop_duplicates()函数的...
You can keep the last occurrence instead of the first when removing duplicates in a Pandas DataFrame. To do this, you can use thedrop_duplicates()function with thekeep='last'argument. Is the operation in-place? By default, thedrop_duplicates()operation isnot in-place, meaning it returns a...
# keep the last occurrencedf = df.drop_duplicates(subset=["f1","f2"],keep="last") PySpark The dropDuplicates function can be used for removing duplicate rows. df = df.dropDuplicates() It allows checking only some of the columns for determining the duplicate rows. ...
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DataFrame.drop_duplicates(subset: Union[Any, Tuple[Any, …], List[Union[Any, Tuple[Any, …]]], None] = None, keep: str = 'first', inplace: bool = False)→ Optional[pyspark.pandas.frame.DataFrame]返回DataFrame,并删除重复行,可以选择仅考虑某些列。 参数: subset:列标签或标签序列,可选 ...
df2 = df.apply(lambda x: x.astype(str).str.lower()).drop_duplicates(subset=['Courses', 'Fee'], keep='first') print(df2) # Output: # Courses Fee Duration Discount # 0 Spark 20000 30days 1000 # 1 PySpark 25000 40days 2300
How do I keep the first occurrence and remove the rest? By default, both methods above keep the first occurrence and remove subsequent duplicates. If you want a different behavior (e.g., keeping the last occurrence), you can adjust it using thekeepparameter. ...
PySpark distinct() transformation is used to drop/remove the duplicate rows (all columns) from DataFrame and dropDuplicates() is used to drop rows based