pandas.DataFrame.rank
DataFrame.rank(axis=0, method=’average’, numeric_only=NoDefault.no_default, na_option=’keep’, ascending=True, pct=False)
参数解释
- axis: axis=0为按行排名，axis=1为按列排名
- method: 如何对具有相同价值(即领带)的记录组进行排序:
- numeric_only: 对于DataFrame对象，如果设置为True，则只对数字列排序。
- na_option: 如何对NaN值进行排序:
- ascending: 元素按升序/降序排列
- pct: 是否以百分比形式显示返回的排名。
Example
df = pd.DataFrame(data={'Animal': ['cat', 'penguin', 'dog',
'spider', 'snake'],
'Number_legs': [4, 2, 4, 8, np.nan]})
method: {‘average’, ‘min’, ‘max’, ‘first’, ‘dense’}, default ‘average’
- average: 组里的平均排名
- min: 组里的最低排名
- max: 组里的最高排名
- first: 按照他们在数组中出现的顺序排列
- dense: 就像’ min ‘一样，但是在组之间rank总是增加1。
df['method_average'] = df['Number_legs'].rank(method='average')
df['method_min'] = df['Number_legs'].rank(method='min')
df['method_max'] = df['Number_legs'].rank(method='max')
df['method_first'] = df['Number_legs'].rank(method='first')
df['method_dense'] = df['Number_legs'].rank(method='dense')
na_option: {‘keep’, ‘top’, ‘bottom’}, default ‘keep’
- keep: 空值排序仍为空值
- top: 排序放在第一位
- bottom: 排在最后一位
df['na_keep'] = df['Number_legs'].rank(na_option='keep')
df['na_top'] = df['Number_legs'].rank(na_option='top')
df['na_bottom'] = df['Number_legs'].rank(na_option='bottom')
ascending: 升序为True， 降序为False
df['asc_True'] = df['Number_legs'].rank(method='min', ascending=True)
df['asc_False'] = df['Number_legs'].rank(method='min', ascending=False)
pct: 是否显示百分比
df['pct_True'] = df['Number_legs'].rank(method='min', pct=True)
df['pct_False'] = df['Number_legs'].rank(method='min', pct=False)
分组排序
pandas.core.groupby.GroupBy.rank 官方文档
Example
df = pd.DataFrame(
{"group": ["a", "a", "a", "a", "a", "b", "b", "b", "b", "b"],
"value": [2, 4, 2, 3, 5, 1, 2, 4, 1, 5],}
)
for method in ['average', 'min', 'max', 'dense', 'first']:
df[f'{method}_rank'] = df.groupby('group')['value'].rank(method)
Original: https://blog.csdn.net/weixin_46599926/article/details/123873077
Author: 羊羊猪
Title: dataframe排序 pd.rank()
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