Question

我有一个包含重复项的数据集：

#Count number of injuries by levels for each players
levelcount = df.groupby(['Relinquished','Severity']).size().reset_index(name='count')
levelcount['DTD'] = ''
levelcount['DNP'] = ''
levelcount['out indefinitely'] = ''
levelcount['ou t for season'] = ''
levelcount.head(4)

   Relinquished  Severity Count DTD DNP outindefinitely outforseason
0   player1         1       1
1   player1         3       1
2   player2         3       1
3   player3         1       3

我想以适当的顺序将计数值添加到其他数据框中：
1：DTD，
2：DNP，
3：无限期出局，
4：参加本赛季

我尝试使用if语句，但似乎无法取得突破。预先谢谢你！

if levelcount['Severity'] == 1:
    df_extension['DTD'] = levelcount[''] 
if levelcount['Severity'] == 2:
    df_extension['DNP'] = levelcount[''] 
if levelcount['Severity'] == 3:
    df_extension['out indefinitely'] = levelcount[''] 
if levelcount['Severity'] == 4:
    df_extension['out for season'] = levelcount['']

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Answer 1

将Series.map与字典一起用于新列，由DataFrame.set_index附加到索引，并由Series.unstack整形：

levelcount = df.groupby(['Relinquished','Severity']).size().reset_index(name='count')
d = {1:'DTD',2:'DNP',3:'outindefinitely',4:'outforseason'}

new = levelcount.set_index(levelcount['Severity'].map(d), append=True)['Count'].unstack()
levelcount = levelcount.join(new.reindex(list(d.values()), axis=1))
print (levelcount)
  Relinquished  Severity  Count  DTD  DNP  outindefinitely  outforseason
0      player1         1      1  1.0  NaN              NaN           NaN
1      player1         3      1  NaN  NaN              1.0           NaN
2      player2         3      1  NaN  NaN              1.0           NaN
3      player3         1      3  3.0  NaN              NaN           NaN

您的解决方案可以按字典循环并设置新列：

levelcount = df.groupby(['Relinquished','Severity']).size().reset_index(name='count')
d = {1:'DTD',2:'DNP',3:'outindefinitely',4:'outforseason'}

for k, v in d.items():
    levecount = levelcount.loc[levelcount['Severity'] == k, v] = levelcount['count']

大熊猫将数据从一个df带到另一个问题

1 个答案: