self) -> 1288 cacher_needs_updating = self._check_is_chained_assignment_possible() 1289 1290 if key is Ellipsis: 1291 key = slice(None) ~/work/pandas/pandas/pandas/core/series.py in ?(
->1121returnself._get_value(key)1123# Convert generator to list before going through hashable part1124# (We will iterate through the generator there to check for slices)1125ifis_iterator(key): File ~/work/pandas/pandas/pandas/core/series.py:1237,inSeries._get_value(self, label, takeable)1...
validate_key(key, axis)-> 1411 return self._get_slice_axis(key, axis=axis)1412 elif com.is_bool_indexer(key):1413 return self._getbool_axis(key, axis=axis)File ~/work/pandas/pandas/pandas/core/indexing.py:1443, in _LocIndexer._get_slice_axis(self, slice_obj, axis)1440 return obj....
Use a.empty, a.bool(), a.item(), a.any() or a.all(). 你需要明确选择你想要对 DataFrame 做什么,例如使用 any()、all() 或empty()。或者,你可能想要比较 pandas 对象是否为 None: In [12]: if pd.Series([False, True, False]) is not None: ...: print("I was not None") ......
In [1]: import numpy as np In [2]: import pandas as pd 从根本上说,数据对齐是固有的。除非您明确这样做,否则标签和数据之间的链接不会被打破。 我们将简要介绍数据结构,然后考虑所有广泛功能和方法的各个类别在单独的部分中。 Series Series是一个能够容纳任何数据类型(整数、字符串、浮点数、Python 对象...
一个带有对 2 列赋值的 if-then: In [5]: df.loc[df.AAA >=5, ["BBB","CCC"]] =555In [6]: df Out[6]: AAA BBB CCC0410100155555552655555537555555 添加另一行具有不同逻辑,以执行-else In [7]: df.loc[df.AAA <5, ["BBB","CCC"]] =2000In [8]: df ...
序列基于索引进行排序:series.sort_index(ascending=True) ; 返回某列的元素的排名:df['scores_rank'] = df['scores'].rank(); 二、索引值/列的名称定义、查看、引用、修改 返回某些列最大值,所在行的索引:df.idxmax(),最小值min类似; 返回索引的值:index.values; 检查索引值是否唯一:index.is_unique(...
returnpd.Series(['🟥'ifitem == row_data.minelse'🟩'ifitem == row_data.maxelse'⬜'foriteminrow_data]) defget_conditional_table_column(data, bins=3, emoji='circle'): tmp = data.copy forcolumnindata.columns: ifpd.api.types.is_numeric_dtype(data[column]): ...
# outcome1.apply(lambda x: [math.ceil(item) for item in x]) 对于满足特定条件的单元格赋值: data.loc[data['age']>80,'comment']=1 把连续数据处理成离散数据,或者把细粒度的离散数据处理成粗粒度的离散数据: pd.cut(x, bins, right=True, labels=...) ...
validate : str, optional If specified, checks if merge is of specified type. * "one_to_one" or "1:1": check if merge keys are unique in both left and right datasets. * "one_to_many" or "1:m": check if merge keys are unique in left dataset. * "many_to_one" or "...