根據字串中定義的條件填充新列

我有條件填充在字串中定義的新列。

condition_string =  "colA='yes' & colB='yes' & (colC='yes' | colD='yes'): 'Yes', colA='no' & colB='no' & (colC='no' | colD='no'): 'No', ELSE : 'UNKNOWN'"

字串可以以任何其他格式（字典）重寫/結構化，然后輸入代碼以獲得最終結果。

資料框是

df = pd.DataFrame(
    {
            'ID': ['AB01', 'AB02', 'AB03', 'AB03', 'AB04','AB05', 'AB06'],
            'colA': ["yes","yes",'yes',"no","no",'yes', np.nan],
            'colB': [np.nan,'yes','yes',"no",'no', np.nan, "yes"],
            'colC': ["yes",'yes', 'yes',"no", "no",np.nan,np.nan],
            'colD': ["yes",'no', 'yes',"no",np.nan,"no",np.nan],
    }
    )

最終結果應該是這樣的

如何在不硬編碼condition_string. 或者你有什么方法condition_string可以重組然后應用于資料幀？

更新：如果字典是這樣的呢？

condition_string =  "colA='yes' & (colB='yes' | colB='no)' & 
(colC='yes' | colD='yes'): 'Yes', colA='no' & colB='no' & (colC='no' |    colD='no'): 'No', ELSE : 'UNKNOWN'"

資料框就像

df = pd.DataFrame(
    {
            'ID': ['AB01', 'AB02', 'AB03', 'AB03', 'AB04','AB05', 'AB06'],
            'colA': ["yes","yes",'yes',"no","no",'yes', np.nan],
            'colB': ["no",'yes','yes',"no",'no', np.nan, "yes"],
            'colC': ["yes",'yes', 'yes',"no", "no",np.nan,np.nan],
            'colD': ["yes",'no', 'yes',"no",np.nan,"no",np.nan]
    }
    )

uj5u.com熱心網友回復：

您可以使用np.where：

df['results'] =  np.where((((df['colA']=='yes') & (df['colB']=='yes')) & ((df['colC']=='yes') | (df['colD']=='yes'))), 'Yes',np.where(((df['colA']=='no') & (df['colB']=='no')) & ((df['colC']=='no' )| (df['colD']=='no')), 'No','UNKNOWN'))

這使：

 ID colA colB colC colD decision
0  AB01  yes  NaN  yes  yes  UNKNOWN
1  AB02  yes  yes  yes   no      Yes
2  AB03  yes  yes  yes  yes      Yes
3  AB03   no   no   no   no       No
4  AB04   no   no   no  NaN       No
5  AB05  yes  NaN  NaN   no  UNKNOWN
6  AB06  NaN  yes  NaN  NaN  UNKNOWN

uj5u.com熱心網友回復：

IIUC 你想為你的 創造任意條件df，這可以使用functools.reduce和來完成operator.and_。然后，您可以使用兩個串列（而不是 dict）來設定條件，第一個是列，第二個是要測驗的字串，最后是np.select：

from functools import reduce
from operator import and_

cols = ["colA", "colB", ["colC", "colD"]] # group the cols in a list if they belong to the same group
answer = ["yes", "no"]

conds = [reduce(and_, [df[i].eq(ans) if isinstance(i, str) else df[i].eq(ans).any(1)
                       for i in cols]) for ans in answer]

df["result"] = np.select(conds, answer, "Unknown")

print (df)

     ID colA colB colC colD   result
0  AB01  yes  NaN  yes  yes  Unknown
1  AB02  yes  yes  yes   no      yes
2  AB03  yes  yes  yes  yes      yes
3  AB03   no   no   no   no       no
4  AB04   no   no   no  NaN       no
5  AB05  yes  NaN  NaN   no  Unknown
6  AB06  NaN  yes  NaN  NaN  Unknown

現在您只需要編輯這兩個串列，cols以及answer是否需要調整您的條件。

uj5u.com熱心網友回復：

這是將您的條件轉換為 python 函式然后將其應用于 DataFrame 的行的解決方案：

import re

condition_string =  "colA='yes' & colB='yes' & (colC='yes' | colD='yes'): 'Yes', colA='no' & colB='no' & (colC='no' | colD='no'): 'No', ELSE : 'UNKNOWN'"

# formatting string as python function apply_cond
col_pattern = re.compile(r'(\w )=')
cond_form = re.sub(r', (?!ELSE)', '\n\telif ', condition_string) \
            .replace(": ", ":\n\t\treturn ") \
            .replace(", ELSE ", "\n\telse") \
            .replace("&", "and") \
            .replace('|', 'or')
cond_form = re.sub(col_pattern, r"row['\1']==", cond_form)
function_def = "def apply_cond(row):\n\tif "   cond_form
#print(function_def) # uncomment to see how the function is defined

# executing the function definition of apply_cond
exec(function_def)

# applying the function to each row
df["result"]=df.apply(lambda x: apply_cond(x), axis=1)

print(df)

輸出：

     ID colA colB colC colD   result
0  AB01  yes  NaN  yes  yes  UNKNOWN
1  AB02  yes  yes  yes   no      Yes
2  AB03  yes  yes  yes  yes      Yes
3  AB03   no   no   no   no       No
4  AB04   no   no   no  NaN       No
5  AB05  yes  NaN  NaN   no  UNKNOWN
6  AB06  NaN  yes  NaN  NaN  UNKNOWN

您可能希望根據以下情況調整字串格式condition_string（我很快就完成了，可能有一些不受支持的組合）但是如果您自動獲取這些字串，它將使您免于重新定義它們。

標籤：Python 熊猫 数据框 麻木的 数据处理

上一篇：如何逐個單元地填充具有回圈的零列的空熊貓資料框？

下一篇：看不到2個資料幀之間的區別