[FIXED] How can I move the location points in numbers or strings of a DataFrame column in Python?

Issue

First of all I apologize for my lousy explanation, I really wanted to turn the thousands and hundreds of the DataFrame into tens as the stock price data was wrong on the csv. Finally I have managed to solve it in the Close column thanks to the response of @Vincent, although I think that it is still not the most orthodox and clean way. Thank you very much for responding.

                   Open         High          Low        Close    Adj Close  \
Date                                                                          
2014-10-31    25.350000    25.350000    25.350000    25.350000    24.343254   
2015-03-31    27.299999    27.299999    27.299999    27.299999    26.215811   
2015-04-30    28.020000    28.020000    28.020000    28.020000    26.907215   
2015-06-30    27.230000    27.230000    27.230000    27.230000    26.148592   
2015-07-31    29.030001    29.030001    29.030001    29.030001    27.877106   
2015-09-30    23.059999    23.059999    23.059999    23.059999    22.144196   
2015-11-30    20.889999    20.889999    20.889999    20.889999    20.060377   
2016-02-29    16.780001    16.780001    16.780001    16.780001    16.113602   
2016-03-31    15.600000    15.600000    15.600000    15.600000    14.980463   
2016-05-31    17.070000    17.070000    17.070000    17.070000    16.392086   
2016-06-30    16.540001    16.540001    16.540001    16.540001    15.883134   
2016-08-31    17.969999    17.969999    17.969999    17.969999    17.256340   
2016-09-30    17.030001    17.030001    17.030001    17.030001    16.353674   
2016-10-31    16.250000    16.250000    16.250000    16.250000    15.604650   
2016-11-30    18.129999    18.129999    18.129999    18.129999    17.409985   
2017-01-31    18.150000    18.150000    18.150000    18.150000    17.429192   
2017-02-28    18.250000    18.250000    18.250000    18.250000    17.525223   
2017-03-10   970.000000   987.500000   970.000000   983.000000   943.961243   
2017-03-13   983.000000   983.000000   983.000000   983.000000   943.961243   
2017-03-14   983.000000   983.000000   983.000000   983.000000   943.961243   
2017-03-15   983.000000   983.000000   983.000000   983.000000   943.961243   
2017-03-16   983.000000   983.000000   983.000000   983.000000   943.961243   
2017-03-17   983.000000   983.000000   983.000000   983.000000   943.961243   
2017-03-20   983.000000   983.000000   983.000000   983.000000   943.961243   
2017-03-21   983.000000   983.000000   983.000000   983.000000   943.961243   
2017-03-22   983.000000   983.000000   983.000000   983.000000   943.961243   
2017-03-23   983.000000   983.000000   983.000000   983.000000   943.961243   
2017-03-24   983.000000   983.000000   983.000000   983.000000   943.961243   
2017-03-27   983.000000   983.000000   983.000000   983.000000   943.961243   
2017-03-28   983.000000   983.000000   983.000000   983.000000   943.961243   
2017-03-29   983.000000   983.000000   983.000000   983.000000   943.961243   
2017-03-30   983.000000   983.000000   983.000000   983.000000   943.961243   
2017-03-31   983.000000   983.000000   983.000000   983.000000   943.961243   
2017-04-03   983.000000   983.000000   983.000000   983.000000   943.961243   
2017-04-04   983.000000   983.000000   983.000000   983.000000   943.961243   
2017-04-05   983.000000   983.000000   983.000000   983.000000   943.961243   
2017-04-06   983.000000   983.000000   983.000000   983.000000   943.961243   
2017-04-07   983.000000   983.000000   983.000000   983.000000   943.961243   
2017-04-10   983.000000   983.000000   983.000000   983.000000   943.961243   
2017-04-11   983.000000   983.000000   983.000000   983.000000   943.961243   
2017-04-12   983.000000   983.000000   983.000000   983.000000   943.961243   
2017-04-13   983.000000   983.000000   983.000000   983.000000   943.961243   
2017-04-18   983.000000   983.000000   983.000000   983.000000   943.961243   
2017-04-19   983.000000   983.000000   983.000000   983.000000   943.961243   
2017-04-20   983.000000   983.000000   983.000000   983.000000   943.961243   
2017-04-21   983.000000   983.000000   983.000000   983.000000   943.961243   
2017-04-24   983.000000   983.000000   983.000000   983.000000   943.961243   
2017-04-25   983.000000   983.000000   983.000000   983.000000   943.961243   
2017-04-26   983.000000   983.000000   983.000000   983.000000   943.961243   
2017-04-27   983.000000   983.000000   983.000000   983.000000   943.961243   
2017-04-28   983.000000   983.000000   983.000000   983.000000   943.961243   
2017-05-02  1228.000000  1230.000000  1221.000000  1220.000000  1171.549072   
2017-05-03  1215.000000  1225.500000  1213.000000  1221.000000  1172.509399   
2017-05-04  1230.000000  1236.319946  1225.000000  1229.000000  1180.191650   
2017-05-05  1233.000000  1233.000000  1213.719971  1214.000000  1165.787354   
2017-05-08  1215.000000  1219.719971  1204.000000  1211.000000  1162.906494   

This is my code:

df = pd.read_csv('psh.csv')
df.set_index('Date', inplace=True)
df.index = pd.to_datetime(df.index)
df.ffill(inplace=True)

close = []
for i in df['Close']:
    if i > 100:
        i = i/100
    close.append(i)

df['Close'] = close

And now I have the Close column like I wanted:

             Open     High         Low      Close   
Date                        
2014-10-31  25.35   25.350000   25.350000   25.35   
2014-11-03  25.35   25.350000   25.350000   25.35   
2014-11-04  25.35   25.350000   25.350000   25.35   
2014-11-05  25.35   25.350000   25.350000   25.35   
2014-11-06  25.35   25.350000   25.350000   25.35   
... ... ... ... ... ... ...
2020-08-17  1948.00 1948.000000 1908.959961 19.30   
2020-08-18  1924.00 1930.000000 1908.000000 19.20   
2020-08-19  1916.00 1932.000000 1910.000000 19.32   
2020-08-20  1912.00 1948.000000 1912.000000 19.30   
2020-08-21  1930.00 1944.910034 1924.000000 19.42   

Solution

Do you just want to modify the value of some of your data inside your file? I am no expert in CSV but from what I remember you are just manipulating a list of list isn't it?

So if your intent is to have 9.43 inside you last column instead of 943, i.e to divide all the results of you last column by 100 you could still try this I suppose:

import csv
f = open('yourfile.csv')
csv_f = csv.reader(f)
for row in csv_f:
     row[5]=row[5]/100

If you want to also get rid of some of the decimals after the coma you can use "%.3f" % row[5] to round correctly your data.

Is it what you were trying to do?

Answered By - Vincent

Answer Checked By - Willingham (PHPFixing Volunteer)