Pandas循环和附加Failu

Question

所以我试图发送一个VIN的datframe到nhtsa的开源api并获取信息。下面用实际的vin替换df['vin']。但是，当向函数中添加循环并尝试追加时，结果是返回一个空白数据帧，而不是12个左右VIN的信息。我做错什么了

代码如下：

import pandas as pd
import requests
    #develop the data
z = pd.DataFrame(columns = ["vin"], data = ['LHJLC79U58B001633','SZC84294845693987','LFGTCKPA665700387','L8YTCKPV49Y010001',
                                                 'LJ4TCBPV27Y010217','LFGTCKPM481006270','LFGTCKPM581004253','LTBPN8J00DC003107',
                                                 '1A9LPEER3FC596536','1A9LREAR5FC596814','1A9LKEER2GC596611','1A9L0EAH9C596099',
                                                 '22A000018'])

z['manufacturer'] = ['A','A','A','A','B','B','B','B','B','C','C','D','D']



def nhtsa(df):
    '''
    sends VIN to NHTSA for data call
    '''
    for i in df:
        url = 'https://vpic.nhtsa.dot.gov/api/vehicles/DecodeVINValuesBatch/'
        post_fields = {'format': 'json', 'data': df['vin']};
        r = requests.post(url, data=post_fields);
        x = r.json()
        f = pd.DataFrame(x['Results'])
        g = f[['VIN','Make','Manufacturer','ManufacturerId','ManufacturerType', 'Model','ModelYear', 'ABS','VehicleType', 'BodyClass','DisplacementCC','ErrorCode', 'SuggestedVIN']]
        csv = pd.DataFrame()
        csv = csv.append(g, ignore_index = True)
        return csv

nhtsa(z)

Answer 1

好的。所以，多亏了@G.Anderson，我才明白了这一点。首先，空数据帧应该在循环之外。接下来，正如G.Anderson所说的，i没有被叫出。我使用.loc语句来调用i是什么。下面是代码

def nhtsa(df):
    '''
    sends VIN to NHTSA for data call
    '''
    df1 = pd.DataFrame()
    for i in current_trial.index:
        vin = df.loc[i, 'VIN']
        url = 'https://vpic.nhtsa.dot.gov/api/vehicles/DecodeVINValuesBatch/'
        post_fields = {'format': 'json', 'data': vin};
        r = requests.post(url, data=post_fields);
        x = r.json()
        f = pd.DataFrame(x['Results'])
        g = f[['VIN','Make','Manufacturer','ManufacturerId','ManufacturerType', 'Model','ModelYear', 'ABS','VehicleType', 'BodyClass','DisplacementCC','ErrorCode', 'SuggestedVIN']]
        df1 = df1.append(g)
    return df1