Question

我是使用熊猫的新手，我面临一些在jupyter中从系列到数据帧进行格式化的问题。基本上我有一个具有这种结构的系列

0 {“省”：“巴黎”， “ city”：“ Paris”，“ countryCode”：“ FR”，“ floor”：null，“ country”： “ France”，“ route”：“ RUE MONGE”，“ extra”：null，“ coordinates”： [2.35242，48.84477]，“ streetNumber”：“ 55”，“ locationType”：null， “ postalCode”：“ 75005”} 1 {“省”：null，“ city”：“巴黎”， “ countryCode”：“ FR”，“ floor”：“ CPO_BELI_floor_1482430978123”， “国家/地区”：“法国”，“路线”：“ PLACE DU PANTHEON”，“其他”：null， “坐标”：[2.345032，48.845715]，“ streetNumber”：“ 17”， “ locationType”：“户外”，“ postalCode”：“ 75005”} 2 {“省”：null，“城市”：“巴黎”，“国家/地区代码”：“ FR”，“楼层”： “ CPO_BELI_floor_1482430978123”，“国家”：“法国”，“路线”：“ RUE DU BAC”，“额外”：null，“坐标”：[2.327753，48.857124]， “ streetNumber”：“ 35”，“ locationType”：“ OUTDOOR”，“ postalCode”： “ 75007”}

我运行此代码是为了将其转换为数据帧，但id不会将序列分成正确的对应列：

pd.DataFrame(data['fields.geolocation'], index=data.index)

非常感谢您的帮助。

Answer 1

您接近了，需要将每一行转换为list：

df = pd.DataFrame(data['fields.geolocation'].values.tolist(), index=data.index)

示例：

a = [{"province": "Paris", "city": "Paris", "countryCode": "FR", "floor": 'null', "country": "France", "route": "RUE MONGE", "extra": 'null', "coordinates": [2.35242, 48.84477], "streetNumber": "55", "locationType": 'null', "postalCode": "75005"} ,
 {"province": 'null', "city": "Paris", "countryCode": "FR", "floor": "CPO_BELI_floor_1482430978123", "country": "France", "route": "PLACE DU PANTHEON", "extra": 'null', "coordinates": [2.345032, 48.845715], "streetNumber": "17", "locationType": "OUTDOOR", "postalCode": "75005"} ,
 {"province": 'null', "city": "Paris", "countryCode": "FR", "floor": "CPO_BELI_floor_1482430978123", "country": "France", "route": "RUE DU BAC", "extra": 'null', "coordinates": [2.327753, 48.857124], "streetNumber": "35", "locationType": "OUTDOOR", "postalCode": "75007"}]

s = pd.Series(a, index=[2,3,5])
print (s)
2    {'province': 'Paris', 'city': 'Paris', 'countr...
3    {'province': 'null', 'city': 'Paris', 'country...
5    {'province': 'null', 'city': 'Paris', 'country...
dtype: object

df = pd.DataFrame(s.values.tolist(), index=s.index)
print (df)

    city            coordinates country countryCode extra  \
2  Paris    [2.35242, 48.84477]  France          FR  null   
3  Paris  [2.345032, 48.845715]  France          FR  null   
5  Paris  [2.327753, 48.857124]  France          FR  null   

                          floor locationType postalCode province  \
2                          null         null      75005    Paris   
3  CPO_BELI_floor_1482430978123      OUTDOOR      75005     null   
5  CPO_BELI_floor_1482430978123      OUTDOOR      75007     null   

               route streetNumber  
2          RUE MONGE           55  
3  PLACE DU PANTHEON           17  
5         RUE DU BAC           35

Answer 2

尝试将pd.concat与axis=1（link）结合使用：

这是您的系列：

A = {"province": "Paris", "city": "Paris", "countryCode": "FR", "floor": None, "country": "France", "route": "RUE MONGE", "extra": None, "coordinates": [2.35242, 48.84477], "streetNumber": "55", "locationType": None, "postalCode": "75005"}
B = {"province": None, "city": "Paris", "countryCode": "FR", "floor": "CPO_BELI_floor_1482430978123", "country": "France", "route": "PLACE DU PANTHEON", "extra": None, "coordinates": [2.345032, 48.845715], "streetNumber": "17", "locationType": "OUTDOOR", "postalCode": "75005"}
C = {"province": None, "city": "Paris", "countryCode": "FR", "floor": "CPO_BELI_floor_1482430978123", "country": "France", "route": "RUE DU BAC", "extra": None, "coordinates": [2.327753, 48.857124], "streetNumber": "35", "locationType": "OUTDOOR", "postalCode": "75007"}

A_series = pd.Series(A)
B_series = pd.Series(B)
C_series = pd.Series(C)

这样您就可以创建所需的数据框

df = pd.concat([A_series, B_series, C_series], axis=1)
type(df)
pandas.core.frame.DataFrame

希望这会有所帮助。

熊猫系列到python中的数据框

2 个答案: