DataFrame.duplicated(subset=None, keep=first)
返回布尔类型的Series结构表示有重复值的行，True表示是重复值（行）
subset: column label or sequence of labels, optional
可以指定检测某一列是否有重复值。默认将检测pandas数据中是否有重复行
keep: {first, last, False}, default first
first: 对于所有重复值，标记除第一次出现的重复值， 默认。
last: 对于所有重复值，标记除最后一次出现的重复值
False: 标记所有重复值
df = pd.DataFrame({
'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],
'style': ['cup', 'cup', 'cup', 'pack', 'pack'],
'rating': [4, 4, 3.5, 15, 5]
})
df
brand style rating
0 Yum Yum cup 4.0
1 Yum Yum cup 4.0
2 Indomie cup 3.5
3 Indomie pack 15.0
4 Indomie pack 5.0
df.duplicated()
0 False
1 True
2 False
3 False
4 False
dtype: bool
DataFrame.drop_duplicates(subset=None, keep=’first’, inplace=False, ignore_index=False)
返回已去重的DataFrame结构，默认保留第一次出现的行（值）、非原地操作、不为去重后的行添加默认索引
-
subset: column label or sequence of labels, optional Only consider certain columns for identifying duplicates, by default use all of the columns.
-
keep: {‘first’, ‘last’, False}, default ‘first’ 同pandas.DataFrame.duplicated()
-
inplace: bool, default False Whether to drop duplicates in place or to return a copy.
-
ignore_index: bool, default False If True, the resulting axis will be labeled 0, 1, …, n – 1. New in version 1.0.0.
Returns
-
DataFrame or None DataFrame with duplicates removed or None if
inplace=True. -
Series.value_counts(normalize=False, sort=True, ascending=False, bins=None, dropna=True)
统计各种值出现的次数，默认降序排列，以便将次数最多的值（除NA）置顶
index = pd.Index([3, 1, 2, 3, 4, np.nan])
index.value_counts()
3.0 2
2.0 1
4.0 1
1.0 1
dtype: int64
Original: https://blog.csdn.net/what_how_why2020/article/details/114982839
Author: 思想在拧紧
Title: pandas去重函数
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