使用python将多个具有相同标题但不同csv文件名的CSV文件合并为一个文件

Question

I'm new in python ...I have tried to apply this code to merge multiple csv files but it doesn't work..basically, I have a files which contains stock prices with header: date,open,High,low,Close,Adj Close Volume... . but each csv file has a different name: Apl.csv,VIX.csv,FCHI.csv etc.. I would like to merge all these csv files in One.. but I would like to add a new columns which will disclose the name of the csv files example:

stock_id,date,open,High,low,Close,Adj Close Volume with stock_id = apl,Vix etc.. I used this code but I got stuck in line 4 here is the code:

  files = os.listdir() 
  file_list = list() 
  for file in os.listdir():
      if file.endswith(".csv")
      df=pd.read_csv(file,sep=";")
      df['filename'] = file
      file_list.append(df) 
  all_days = pd.concat(file_list, axis=0, ignore_index=True) 
  all_days.to_csv("all.csv")

Someone could help me to sort out this ?

Answer 1

In Python, the indentation level matters, and you need a colon at the end of an if statement. I can't speak to the method you're trying, but you can clean up the synax with this:

files = os.listdir() 
file_list = list() 
for file in os.listdir():
    if file.endswith(".csv"):
        df=pd.read_csv(file,sep=";")
        df['filename'] = file
        file_list.append(df) 
all_days = pd.concat(file_list, axis=0, ignore_index=True) 
all_days.to_csv("all.csv")

Answer 2

I'm relatively new in python ..here is what I'd like to do..I got a folder with multiples csv files ( 2018.csv,2017.csv,2016.csv etc..)500 csv files to be precise.. each csv contains header "date","Code","Cur",Price etc..I'd like to concatenate all 500 csv files in one datafame...here is my code for one csv files but it's very slow , I want to do it for all 500 files and concantanate in one dataframe :

 DB_2017 = pd.read_csv("C:/folder/2018.dat",sep=",", header =None).iloc[: 0,4,5,6]

 DB_2017.columns =["date","Code","Cur",Price]

 DB_2017['Code'] =DB_2017['Code'].map(lambdax:x.lstrip('@').rstrip('@'))

 DB_2017['Cur'] =DB_2017['Cur'].map(lambdax:x.lstrip('@').rstrip('@'))

 DB_2017['date'] =DB_2017['date'].apply(lambdax:pd.timestamp(str(x)[:10)

 DB_2017['Price'] =pd.to_numeric(DB_2017.Price.replace(',',';')