我得到以下结果：
[4 rows x 10 columns]
id ID_ENTIDADE ENTIDADE CHAMADO ... DATA_ALT VALOR_OLD VALOR_NEW PRIORIDADE
695 6802505 136 Professional Services > Ser... 2019000518 ... 2019-01-14 15:21:01 NaN N1 (20) 0
698 6804412 136 Professional Services > Ser... 2019000518 ... 2019-01-14 15:52:46 NaN Contrato 157 (198) 0
699 6804413 136 Professional Services > Ser... 2019000518 ... 2019-01-14 15:52:46 1.0 2 14
700 6804415 136 Professional Services > Ser... 2019000518 ... 2019-01-14 15:52:46 3.0 1 3
701 6804650 136 Professional Services > Ser... 2019000518 ... 2019-01-14 15:53:32 NaN N1 - Security (25) 0
[5 rows x 10 columns]
id ID_ENTIDADE ENTIDADE CHAMADO ... DATA_ALT VALOR_OLD VALOR_NEW PRIORIDADE
705 6805135 136 Professional Services > Ser... 2019000519 ... 2019-01-14 16:02:01 NaN N1 (20) 0
711 6806934 136 Professional Services > Ser... 2019000519 ... 2019-01-14 16:33:41 NaN N1 - Security (25) 0
712 6806936 136 Professional Services > Ser... 2019000519 ... 2019-01-14 16:33:41 1.0 2 14
713 6806938 136 Professional Services > Ser... 2019000519 ... 2019-01-14 16:33:41 3.0 1 3
710 6806932 136 Professional Services > Ser... 2019000519 ... 2019-01-14 16:33:41 NaN Contrato 157 (198) 0
[5 rows x 10 columns]
id ID_ENTIDADE ENTIDADE CHAMADO ... DATA_ALT VALOR_OLD VALOR_NEW PRIORIDADE
717 6808869 105 Professional Services > Sup... 2019000523 ... 2019-01-14 17:05:35 NaN Contrato 135 (136) 0
718 6808870 105 Professional Services > Sup... 2019000523 ... 2019-01-14 17:05:35 NaN N2 - DC (28) 0
757 6810787 105 Professional Services > Sup... 2019000523 ... 2019-01-14 17:41:31 3.0 2 3
[3 rows x 10 columns]
id ID_ENTIDADE ENTIDADE CHAMADO ... DATA_ALT VALOR_OLD VALOR_NEW PRIORIDADE
719 6808990 136 Professional Services > Ser... 2019000524 ... 2019-01-14 17:10:02 NaN N1 (20) 0
720 6809088 136 Professional Services > Ser... 2019000524 ... 2019-01-14 17:12:59 NaN Contrato 157 (198) 0
721 6809090 136 Professional Services > Ser... 2019000524 ... 2019-01-14 17:12:59 NaN N1 - Security (25) 0
722 6809092 136 Professional Services > Ser... 2019000524 ... 2019-01-14 17:12:59 1.0 2 14
723 6809094 136 Professional Services > Ser... 2019000524 ... 2019-01-14 17:12:59 3.0 1 3
[5 rows x 10 columns]
我获得了以下代码：
df = pd.read_csv("csv3.csv", sep=";", encoding = "ISO-8859-1")
df2 = df.sort_values(['CHAMADO', 'id'])
g1 = df2.sort_values(['DATA_ALT'], ascending=True)
ret_group = g1.groupby(['CHAMADO'])
for key, group in ret_group:
if group['PRIORIDADE'].any() == True:
print(group)
但是我需要一个过滤器来检查“ VALOR_NEW”列的前3行是否包含单词“ CONTRATO”。
我无法创建一个可以做到这一点的过滤器，每执行一次过滤器，它只会向我返回第一行出现“ CONTRATO”字样的结果，如下例所示：
id ID_ENTIDADE ENTIDADE CHAMADO ... DATA_ALT VALOR_OLD VALOR_NEW PRIORIDADE
717 6808869 105 Professional Services > Sup... 2019000523 ... 2019-01-14 17:05:35 NaN Contrato 135 (136) 0
718 6808870 105 Professional Services > Sup... 2019000523 ... 2019-01-14 17:05:35 NaN N2 - DC (28) 0
757 6810787 105 Professional Services > Sup... 2019000523 ... 2019-01-14 17:41:31 3.0 2 3
[3 rows x 10 columns]
答案 0 :(得分：1)
您可以使用以下内容：
my_list = list(df.groupby('CHAMADO').apply(lambda x: x[:3][x[:3]['VALOR_NEW'].str.contains('Contrato',na=False)])['CHAMADO'].values)
#[2019000518, 2019000523, 2019000524]
这给出了前三行中包含单词Contrato的组的列表。
>>df[df.CHAMADO.isin(my_list)]
这将为您提供整个数据帧，其中包含CHAMADO下的值，该值在前三行中包含Contrato。
要在单独的数据框中存储值，可以创建一个字典：
dfs = ['df_' + str(x) for x in my_list]
dicdf = dict()
i = 0
while i < len(dfs):
dicdf[dfs[i]] = df[(df['CHAMADO']== my_list[i])]
i = i + 1
print(dicdf)
您可以通过喜欢或在SO中看到的任何其他方法进行存储。
答案 1 :(得分：0)
如果该列是字符串列，则仅按字符串contains过滤值。假设您的df为group。由于您希望所有结果都显示出来，因此您可以制作一个非优雅的解决方案，该解决方案可以制作3个数据帧：
首先，包含CONTRATO的内容：
df1 = group.loc[group['VALOR_NEW'].fillna('nada').str.contains('CONTRATO')].head(3)
然后另一个不出现（注意.loc开头的“〜”，意思相反：
df2 = group.loc[~group['VALOR_NEW'].fillna('nada').str.contains('CONTRATO')]
最后，您首先与CONTRATO的人并存：
df3 = pd.concat([df1,df2])
请注意，它区分大小写，并且我添加了fillna()，因为您无法搜索它是否具有nan值。
编辑：根据您的要求在末尾添加.head(3)，仅获得前3行。
edit2：编辑了原始解决方案
vlwflws
答案 2 :(得分：0)
IIUC，您需要在前三行的'Contrato'列中检查'VALOR_NEW'的分组数据帧。此检查应执行以下操作：
if (group['VALOR_NEW'].head(3) == 'Contrato').any():
print(group)