Python 要查找与dataframe（np.where with.index（））对应的元素的列表索引

我想找到满足条件的字典或列表项的索引，并将其写入dataframe中的新列

我从以下设置开始：

import pandas as pd
import numpy as np
df = pd.DataFrame(data = {'col1': ['2018_08', '2008_02','2019_01','2017_04']})

dates = {0: ['2019-01-15 00:00:00', '2019_01', 1, 2019, 0],
         -1: ['2018-12-15 00:00:00', '2018_12', 12, 2018, -1],
         -2: ['2018-11-15 00:00:00', '2018_11', 11, 2018, -2],
         -3: ['2018-10-15 00:00:00', '2018_10', 10, 2018, -3],
         -4: ['2018-09-15 00:00:00', '2018_09', 9, 2018, -4],
         -5: ['2018-08-15 00:00:00', '2018_08', 8, 2018, -5]}

我想检查数据帧

df

中列

col1

的值是否包含在字典

dates

中。如果是，则返回键或字典中相应列表的最后一个条目。如果不是，则返回NaT或NaN。我试过：

df['month_seq'] = np.where(df.col1.isin([dates[i][1] for i in range(0,-6,-1)]), '?' ,pd.NaT)

它标识正确的条目，但不返回相应的负数。输出内容如下：

    col1    month_seq
0   2018_08     ?
1   2008_02     NaT
2   2019_01     ?
3   2017_04     NaT

如果你试过什么

[dates[i][1] for i in range(0,-6,-1)].index(df.col1)

返回一个错误

提前感谢你的帮助

您可以使用适当的功能（

locate

在本例中）：

输出

0   -5.0
1    NaN
2    0.0
3    NaN
Name: col1, dtype: float64

与字典理解创建的字典一起使用：

df = pd.DataFrame(data = {'col1': ['2018_08', '2008_02','2019_01','2017_04']})

dates = {0: ['2019-01-15 00:00:00', '2019_01', 1, 2019, 0],
         -1: ['2018-12-15 00:00:00', '2018_12', 12, 2018, -1],
         -2: ['2018-11-15 00:00:00', '2018_11', 11, 2018, -2],
         -3: ['2018-10-15 00:00:00', '2018_10', 10, 2018, -3],
         -4: ['2018-09-15 00:00:00', '2018_09', 9, 2018, -4],
         -5: ['2018-08-15 00:00:00', '2018_08', 8, 2018, -5]}

d = {v[1]:k for k, v in dates.items()}
print (d)
{'2019_01': 0, '2018_12': -1, '2018_11': -2, '2018_10': -3, '2018_09': -4, '2018_08': -5}

df['new'] = df['col1'].map(d)
print (df)
      col1  new
0  2018_08 -5.0
1  2008_02  NaN
2  2019_01  0.0
3  2017_04  NaN

---

这似乎是最快的版本。非常感谢。

df = pd.DataFrame(data = {'col1': ['2018_08', '2008_02','2019_01','2017_04']})

dates = {0: ['2019-01-15 00:00:00', '2019_01', 1, 2019, 0],
         -1: ['2018-12-15 00:00:00', '2018_12', 12, 2018, -1],
         -2: ['2018-11-15 00:00:00', '2018_11', 11, 2018, -2],
         -3: ['2018-10-15 00:00:00', '2018_10', 10, 2018, -3],
         -4: ['2018-09-15 00:00:00', '2018_09', 9, 2018, -4],
         -5: ['2018-08-15 00:00:00', '2018_08', 8, 2018, -5]}

d = {v[1]:k for k, v in dates.items()}
print (d)
{'2019_01': 0, '2018_12': -1, '2018_11': -2, '2018_10': -3, '2018_09': -4, '2018_08': -5}

df['new'] = df['col1'].map(d)
print (df)
      col1  new
0  2018_08 -5.0
1  2008_02  NaN
2  2019_01  0.0
3  2017_04  NaN