1

我有以下代码：

dfs = []
    for f in files_xlsx:
            city_name = pd.read_excel(f, "1. City", nrows=1, parse_cols="C", header=None, skiprows=1)
            country_code = pd.read_excel(f, "1. City", nrows=1, parse_cols="C", header=None, skiprows=2)
            data = pd.read_excel(f, "1. City", parse_cols="B:J", header=None, skiprows=8)
            data['City name'] = city_name.iat[0,0]
            data['City code'] = country_code.iat[0,0]
            dfs.append(data)

    df = pd.concat(dfs, ignore_index=True)

91, 92, 93, 94, 95, 96, 97当且仅当每个 Excel 文件包含以下位置（列和行组合）中的值时，我才想运行循环D8, E8, F8, G8, H8, I8, J8。仅当所有文件都满足此条件时，循环才应运行。

我所有的 Excel 文件在理论上都具有相同的格式。实际上，它们通常不会，所以我想在添加它们之前进行检查。如果代码能告诉我哪个文件不满足上述条件，那就太好了。谢谢你。

编辑：

In[1]: data

Out[1]:

0   1   2   3   4   5   6   7   8   City name   City code
0   x   x   x   x   x   x   x   x       x           x
1   x   x   x   x   x   x   x   x       x           x
2   x   x   x   x   x   x   x   x       x           x
4

1 回答 1

0

考虑构建一个类似于 Excel 数据但值与规范匹配的帮助数据框（第 8 行和 DJ 列具有这些特定值）。然后在循环中迭代地合并这个帮助数据框，如果它返回匹配，有条件地附加到您的数据框列表中。

注意：将列调整为实际的列名，替换list('ABCDEFGHIJ')为名称列表['col1','col2','col3',...]、、inDataFrame()和merge()调用。

check_df = pd.concat([pd.DataFrame([[0]*9 for _ in range(7)], 
                                    columns=['Heading code','Heading name','91','92','93','94','95','96','97']),
                      pd.DataFrame([[0,0,91,92,93,94,95,96,97]], 
                                   columns=['Heading code','Heading name','91','92','93','94','95','96','97'])],
                      ignore_index=True).reset_index()

print(check_df)    
#    index  Heading code  Heading name  91  92  93  94  95  96  97
# 0      0             0             0   0   0   0   0   0   0   0
# 1      1             0             0   0   0   0   0   0   0   0
# 2      2             0             0   0   0   0   0   0   0   0
# 3      3             0             0   0   0   0   0   0   0   0
# 4      4             0             0   0   0   0   0   0   0   0
# 5      5             0             0   0   0   0   0   0   0   0
# 6      6             0             0   0   0   0   0   0   0   0
# 7      7             0             0  91  92  93  94  95  96  97

dfs = []
for f in files_xlsx:
   city_name = pd.read_excel(f, "1. City", nrows=1, parse_cols="C", header=None, skiprows=1)
   country_code = pd.read_excel(f, "1. City", nrows=1, parse_cols="C", header=None, skiprows=2)
   data = pd.read_excel(f, "1. City", parse_cols="B:J", header=None, skiprows=8)\
            .assign(city_name=city_name.iat[0,0], city_code=country_code.iat[0,0])
   data.columns = ['Heading code','Heading name','91','92','93','94','95','96','97','City name','City code']

   # INNER JOIN MERGE ON INDEX AND COLS, D-J
   tmp = data.reset_index().merge(check_df, on=['index','91','92','93','94','95','96','97'])    

   # CONDITIONALLY APPEND
   if len(tmp) > 0:
        dfs.append(data)

df = pd.concat(dfs, ignore_index=True)

下面用随机数据演示

np.random.seed(82118)
# LIST OF FIVE DATAFRAMES (TO RESEMBLE EXCEL DFs)
rand_dfs = [pd.DataFrame([np.random.randint(1, 100, 9) for _ in range(10)],
                    columns=['Heading code','Heading name','91','92','93','94','95','96','97'])
            for _ in range(5)]

# UPDATE TWO DATAFRAMES EACH WITH 10 COLS TO INCLUDE MATCHING 8TH ROW
rand_dfs[2].loc[7] = [0, 0, 91, 92, 93, 94, 95, 96, 97]    
rand_dfs[4].loc[7] = [0, 0, 91, 92, 93, 94, 95, 96, 97]

final_dfs = []
for d in rand_dfs:
    tmp = d.reset_index().merge(check_df, on=['index','91','92','93','94','95','96','97'])

    if len(tmp) > 0: 
        final_dfs.append(d)

final_df = pd.concat(final_dfs, ignore_index=True)

输出（见第 8 行和第 17 行匹配标准）

print(final_df)

#     Heading code  Heading name  91  92  93  94  95  96  97
# 0             53            98  67   8  86  33  65  56  62
# 1             61             9  40  14  18   9  53  30  24
# 2             89            88  80  91  91  49   8  39  84
# 3             15            99  49  92  63  96  11  95  29
# 4             13            62  82  12  34  92  54  29  47
# 5             44            18  67  61  52  71  52  25  12
# 6             56            25  52  10  82  12  59  63  15
# 7              0             0  91  92  93  94  95  96  97
# 8             51            50  27  38  34  11  57  92   3
# 9             49            99  46  87  46   5  63  24   8
# 10            31            62   8  23  19  66  60  10  66
# 11            51            98  30  44  45  39  32  74  82
# 12            88            19  54  28  38  71   3  31  34
# 13            58            13  89  17  96  35  12  52  85
# 14            93            67  13  13  28  43  24   7   4
# 15            34            26  73  20  44  37  18  17  22
# 16            59             1  99   9  11   6   4  99  95
# 17             0             0  91  92  93  94  95  96  97
# 18            88             6  23  20  35  26  37  56  51
# 19            21            67  19  63  77  98  41   9  22
于 2018-08-21T14:38:59.413 回答