有 Pandas DataFrame 为:
print(df)
call_id calling_number call_status
1 123 BUSY
2 456 BUSY
3 789 BUSY
4 123 NO_ANSWERED
5 456 NO_ANSWERED
6 789 NO_ANSWERED
在这种情况下,具有不同 call_status 的记录(比如“错误”或其他什么,我无法预测),值可能会出现在数据框中。我需要为这样的值即时添加一个新列。 我已经应用了 pivot_table() 函数并得到了我想要的结果:
df1 = df.pivot_table(df,index='calling_number',columns='status_code', aggfunc = 'count').fillna(0).astype('int64')
calling_number ANSWERED BUSY NO_ANSWER
123 0 1 1
456 0 1 1
789 0 1 1
现在我需要再添加一列,其中包含具有给定 call_number 的已接电话的百分比,计算为 ANSWERED 与总数的比率。 源数据帧 'df' 可能不包含 call_status = 'ANSWERED' 的条目,因此在这种情况下,百分比列自然应为零值。
预期结果是:
calling_number ANSWERED BUSY NO_ANSWER ANS_PERC(%)
123 0 1 1 0
456 0 1 1 0
789 0 1 1 0
答案 0 :(得分:1)
使用crosstab
:
df1 = pd.crosstab(df['calling_number'], df['status_code'])
或者如果需要通过 count
函数使用 NaN
和添加参数 pivot_table
来排除 fill_value=0
:
df1 = df.pivot_table(df,
index='calling_number',
columns='status_code',
aggfunc = 'count',
fill_value=0)
然后对于每行的比率除法求和值:
df1 = df1.div(df1.sum(axis=1), axis=0)
print (df1)
ANSWERED BUSY NO_ANSWER
calling_number
123 0.333333 0.333333 0.333333
456 0.333333 0.333333 0.333333
789 0.333333 0.333333 0.333333
编辑:要添加可能不存在的某些类别,请使用 DataFrame.reindex
:
df1 = (pd.crosstab(df['calling_number'], df['call_status'])
.reindex(columns=['ANSWERED','BUSY','NO_ANSWERED'], fill_value=0))
df1['ANS_PERC(%)'] = df1['ANSWERED'].div(df1['ANSWERED'].sum()).fillna(0)
print (df1)
call_status ANSWERED BUSY NO_ANSWERED ANS_PERC(%)
calling_number
123 0 1 1 0.0
456 0 1 1 0.0
789 0 1 1 0.0
如果需要每行总数:
df1['ANS_PERC(%)'] = df1['ANSWERED'].div(df1.sum(axis=1))
print (df1)
call_status ANSWERED BUSY NO_ANSWERED ANS_PERC(%)
calling_number
123 0 1 1 0.0
456 0 1 1 0.0
789 0 1 1 0.0
编辑 1:
Soluton 将一些错误的值替换为 ERROR
:
print (df)
call_id calling_number call_status
0 1 123 ttt
1 2 456 BUSY
2 3 789 BUSY
3 4 123 NO_ANSWERED
4 5 456 NO_ANSWERED
5 6 789 NO_ANSWERED
L = ['ANSWERED', 'BUSY', 'NO_ANSWERED']
df['call_status'] = df['call_status'].where(df['call_status'].isin(L), 'ERROR')
print (df)
0 1 123 ERROR
1 2 456 BUSY
2 3 789 BUSY
3 4 123 NO_ANSWERED
4 5 456 NO_ANSWERED
5 6 789 NO_ANSWERED
df1 = (pd.crosstab(df['calling_number'], df['call_status'])
.reindex(columns=L + ['ERROR'], fill_value=0))
df1['ANS_PERC(%)'] = df1['ANSWERED'].div(df1.sum(axis=1))
print (df1)
call_status ANSWERED BUSY NO_ANSWERED ERROR ANS_PERC(%)
calling_number
123 0 0 1 1 0.0
456 0 1 1 0 0.0
789 0 1 1 0 0.0
答案 1 :(得分:0)
我喜欢 cross_tab 的想法,但我喜欢列操作,因此很容易参考:
# define a function to capture all the other call_statuses into one bucket
def tester(x):
if x not in ['ANSWERED', 'BUSY', 'NO_ANSWERED']:
return 'OTHER'
else:
return x
#capture the simplified status in a new column
df['refined_status'] = df['call_status'].apply(tester)
#Do the pivot (or cross tab) to capture the sums:
df1= df.pivot_table(values="call_id", index = 'calling_number', columns='refined_status', aggfunc='count')
#Apply a division to get the percentages:
df1["TOTAL"] = df1[['ANSWERED', 'BUSY', 'NO_ANSWERED', 'OTHER']].sum(axis=1)
df1["ANS_PERC"] = df1["ANSWERED"]/df1.TOTAL * 100
print(df1)