如何通过for循环填充熊猫数据框？

Question

问题尤其与此功能有关

def jsontodataframe(): #collect OHLC data from scstrade

    companies = {'Habib Bank Limited':'HBL','Engro Chemical':'ENGRO'}
    url = 'http://www.scstrade.com/stockscreening/SS_CompanySnapShotHP.aspx/chart'

    payload = {"date1":"01/01/2019","date2":"06/01/2019","rows":20,"page":1,"sidx":"trading_Date",
    "sord":"desc"}

    for company in companies:
        payload["par"] = companies[company]
        #print(payload)
        json_data = requests.post(url, json=payload).json() #download the json POST request from scstrade
        json_normalize(json_data)
        df = pd.DataFrame(json_data) #convert the json to pandas dataframe
        df = pd.io.json.json_normalize(json_data['d'], errors='ignore')
        df.columns = ['Date', 'Open', 'High', 'Low', 'Close', 'Volume', 'Change'] #rename the columns to better names

        df['Date'] = df['Date'].str.strip('/Date()')
        df['Date'] = pd.to_datetime(df['Date'], origin='unix', unit='ms') #convert unix timestamp to pandas datetime and set the index
        df['ID'] = companies[company]
        df.set_index(['ID'], inplace=True)
        print(df.head())

    df.to_csv("OHLC_values.csv") #save .csv file for later usage

目前， df变量每次都会被覆盖，而我的输出是这样的：

我考虑过使用append，但这会严重影响性能，并且我希望代码尽可能高效（以便以后可以轻松扩展）。 现在df.columns行是多余的，所以我应该只在for循环之外定义数据帧吗？ 但是那个json_normalize函数将引入自己的列名，所以这是必要的。

理想情况下，我只想要一个大数据框，然后将其转换为一个.csv文件

Answer 1

理想情况下，我只想要一个大数据框，然后将其转换为一个.csv文件

这可以使用pandas.concat实现

import calendar, requests
import pandas as pd
from pandas.io.json import json_normalize

def jsontodataframe():

    companies = {'Habib Bank Limited':'HBL','Engro Chemical':'ENGRO'}
    url = 'http://www.scstrade.com/stockscreening/SS_CompanySnapShotHP.aspx/chart'

    payload = {"date1":"01/01/2019","date2":"06/01/2019","rows":20,"page":1,"sidx":"trading_Date",
    "sord":"desc"}
    data = []
    for company in companies:
        payload["par"] = companies[company]

        json_data = requests.post(url, json=payload).json() 
        json_normalize(json_data)
        df = pd.DataFrame(json_data) 
        df = pd.io.json.json_normalize(json_data['d'], errors='ignore')
        df.columns = ['Date', 'Open', 'High', 'Low', 'Close', 'Volume', 'Change'] 

        df['Date'] = df['Date'].str.strip('/Date()')
        df['Date'] = pd.to_datetime(df['Date'], origin='unix', unit='ms') 

        df['Date'] = df['Date'].dt.floor('d') # return only dates

        ### update
        df['Month_as_number'] = df['Date'].dt.month # created a column with a month as number - 5, 11 etc.
        df['Month_as_name'] = df['Month_as_number'].apply(lambda x: calendar.month_abbr[x]) # 5 as May etc
        ###

        df['ID'] = companies[company]
        df.set_index(['ID'], inplace=True)
        data.append(df)
    # save to csv instead of returning dataframe    
    pd.concat(data).to_csv('OHLC_values.csv', index=False)

---

所以我编辑了最初的答案。 现在，该功能将数据帧保存到.csv文件。 另外，我将date列剥离为仅日期。