![](/img/trans.png)
[英]Pandas Create new column based on a count and a condition from another dataframe
[英]create a new column in pandas dataframe using if condition from another dataframe
我有两个数据框如下
transactions
buy_date buy_price
0 2018-04-16 33.23
1 2018-05-09 33.51
2 2018-07-03 32.74
3 2018-08-02 33.68
4 2019-04-03 33.58
和
cii
from_fy to_fy score
0 2001-04-01 2002-03-31 100
1 2002-04-01 2003-03-31 105
2 2003-04-01 2004-03-31 109
3 2004-04-01 2005-03-31 113
4 2005-04-01 2006-03-31 117
在交易数据cii_score
我需要根据以下条件创建一个新列cii_score
如果transactions['buy_date']
介于cii['from_fy']
和cii['to_fy']
取transactions['cii_score']
的cii['score']
值
我试过列表理解,但它不好。
请求您的意见来解决这个问题。
首先,我们设置您的 dfs。 注意我在这个简短的例子中修改了transactions
中的日期以使其更有趣
import pandas as pd
from io import StringIO
trans_data = StringIO(
"""
,buy_date,buy_price
0,2001-04-16,33.23
1,2001-05-09,33.51
2,2002-07-03,32.74
3,2003-08-02,33.68
4,2003-04-03,33.58
"""
)
cii_data = StringIO(
"""
,from_fy,to_fy,score
0,2001-04-01,2002-03-31,100
1,2002-04-01,2003-03-31,105
2,2003-04-01,2004-03-31,109
3,2004-04-01,2005-03-31,113
4,2005-04-01,2006-03-31,117
"""
)
tr_df = pd.read_csv(trans_data, index_col = 0)
tr_df['buy_date'] = pd.to_datetime(tr_df['buy_date'])
cii_df = pd.read_csv(cii_data, index_col = 0)
cii_df['from_fy'] = pd.to_datetime(cii_df['from_fy'])
cii_df['to_fy'] = pd.to_datetime(cii_df['to_fy'])
主要是下面的计算:对于tr_df
每个行索引,找到cii_df
中满足条件的行的索引。 下面计算这个匹配,列表的每个元素都等于cii_df
的适当行索引:
match = [ [(f<=d) & (d<=e) for f,e in zip(cii_df['from_fy'],cii_df['to_fy']) ].index(True) for d in tr_df['buy_date']]
match
产生
[0, 0, 1, 2, 2]
现在我们可以合并了
tr_df.merge(cii_df, left_on = np.array(match), right_index = True)
以便我们得到
key_0 buy_date buy_price from_fy to_fy score
0 0 2001-04-16 33.23 2001-04-01 2002-03-31 100
1 0 2001-05-09 33.51 2001-04-01 2002-03-31 100
2 1 2002-07-03 32.74 2002-04-01 2003-03-31 105
3 2 2003-08-02 33.68 2003-04-01 2004-03-31 109
4 2 2003-04-03 33.58 2003-04-01 2004-03-31 109
score
列就是你要求的
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