对于多列，将当前行和上一行的差异附加到新列

对于我的df中的每一列，我想从上一行（行[n 1] -row [n]）中减去当前行，但是我遇到了困难 .

我的代码如下：

#!/usr/bin/python3
from pandas_datareader import data
import pandas as pd
import fix_yahoo_finance as yf
yf.pdr_override()
import os


stock_list = ["BHP.AX", "CBA.AX", "RHC.AX", "TLS.AX", "WOW.AX", "^AORD"]

# Function to get the closing price of the individual stocks
# from the stock_list list
def get_closing_price(stock_name, specific_close):
    symbol = stock_name
    start_date = '2010-01-01'
    end_date = '2016-06-01'
    df = data.get_data_yahoo(symbol, start_date, end_date)
    sym = symbol + " "
    print(sym * 10)
    df = df.drop(['Open', 'High', 'Low', 'Adj Close', 'Volume'], axis=1)
    df = df.rename(columns={'Close': specific_close})
    # https://stackoverflow.com/questions/16729483/converting-strings-to-floats-in-a-dataframe
    # df[specific_close] = df[specific_close].astype('float64')
    print(type(df[specific_close]))
    return df

# Creates a big DataFrame with all the stock's Closing
# Price returns the DataFrame
def get_all_close_prices(directory):
    count = 0
    for stock_name in stock_list:
        specific_close = stock_name.replace(".AX", "") + "_Close"
        if not count:
            prev_df = get_closing_price(stock_name, specific_close)
        else:
            new_df = get_closing_price(stock_name, specific_close)
            # https://stackoverflow.com/questions/11637384/pandas-join-merge-concat-two-dataframes
            prev_df = prev_df.join(new_df)
        count += 1
    prev_df.to_csv(directory)
    return prev_df

# THIS IS THE FUNCTION I NEED HELP WITH
# AS DESCRIBED IN THE QUESTION
def calculate_return(df):
    count = 0
    # for index, row in df.iterrows():
    print(df.columns[0])
    for stock in stock_list:
        specific_close = stock.replace(".AX", "") + "_Close"
        print(specific_close)
        # https://stackoverflow.com/questions/15891038/change-data-type-of-columns-in-pandas
        pd.to_numeric(specific_close, errors='ignore')
        df.columns[count].diff()
        count += 1
     return df


def main():
    # FINDS THE CURRENT DIRECTORY AND CREATES THE CSV TO DUMP THE DF
    csv_in_current_directory = os.getcwd() + "/stk_output.csv"

    # FUNCTION THAT GETS ALL THE CLOSING PRICES OF THE STOCKS
    # AND RETURNS IT AS ONE COMPLETE DATAFRAME
    df = get_all_close_prices(csv_in_current_directory)

    # THIS PRINTS OUT WHAT IS IN "OUTPUT 1"
    print(df)

    # THIS FUNCTION IS WHERE I HAVE THE PROBLEM
    df = calculate_return(df)

    # THIS SHOULD PRINT OUT WHAT IS IN "EXPECTED OUTPUT"
    print(df)




# Main line of code
if __name__ == "__main__":
    main()

Question:

对于每一列，我想从前一行（行[n 1] -row [n]）中减去当前行，并将此值分配给数据帧末尾的新列，作为 stock_name + "_Earning" 的新列 . 我的预期输出（参见：预期输出）是我仍然有原始的 df ，如 Output 1 所示，但有6个额外的列，第一行是空的，以及行的差异（行[n 1] -row [n ]）在各栏中 .

Problem Faced:

使用当前代码 - 我收到以下错误，我试图摆脱

AttributeError：'str'对象没有属性'diff'

Things I Have Tried:

我试过的一些事情：

• Change data type of columns in Pandas

• numpy.diff

• Data-frame Object has no Attribute

• How do I subtract the previous row from the current row in a pandas dataframe and apply it to every row; without using a loop?

• pandas.DataFrame.diff

Expected Output:

BHP_Close  CBA_Close  RHC_Close  TLS_Close  WOW_Close        ^AORD  BHP_Earning  CBA_Earning  RHC_Earning  TLS_Earning  WOW_Earning  ^AORD_Earning
Date
2010-01-03  40.255699  54.574299  11.240000       3.45  27.847300  4889.799805
2010-01-04  40.442600  55.399799  11.030000       3.44  27.679100  4939.500000     0.186901       0.8255        -0.21        -0.01      -0.1682   49.70020000

Output 1:

BHP_Close  CBA_Close  RHC_Close  TLS_Close  WOW_Close  ^AORD_Close
Date
2010-01-03  40.255699  54.574299  11.240000       3.45  27.847300  4889.799805
2010-01-04  40.442600  55.399799  11.030000       3.44  27.679100  4939.500000
2010-01-05  40.947201  55.678299  11.180000       3.38  27.629601  4946.799805
...               ...        ...        ...        ...        ...          ...
2016-05-30  19.240000  78.180000  72.730003       5.67  22.389999  5473.600098
2016-05-31  19.080000  77.430000  72.750000       5.59  22.120001  5447.799805
2016-06-01  18.490000  76.500000  72.150002       5.52  21.799999  5395.200195