Pandas replace function not working on datafram with floats

[bcrypto]

Hi,

I am using Pandas' replace function and it works on one DataFrame but not on another and I don't understand why. I have tried many different solutions but nothing seems to work - Please take a look at the example code below:

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

import pandas as pd import talib as ta   rslt_df = pd.DataFrame() test_df = pd.DataFrame()   df = pd.DataFrame({'Close': [1, 2.3842, 5.389132, 4.09712373, 5.90845, 6.0981234, 7, 8, 9]})   # ROC------------------------------------------------- for i in range(2):     if i != 0:         rslt_df['roc%i' % i] = ta.ROC(df['Close'].shift(1), timeperiod=i)   # replace rtest_df print("rslt_df:\n", rslt_df) print("dtypes= ", rslt_df.dtypes) # rslt_df = rslt_df.fillna(0) rslt_df = rslt_df.replace(to_replace=[138.420000, 126.035232, 44.209704], value=[11111, 22222, 33333])  # This one does NOT replace all of them print("rslt_df:\n", rslt_df)   # replace roc df--------------------- print("dtypes2= ", test_df.dtypes) test_df = df.replace(to_replace=[1, 9], value=[33333, 44444])   print("test_df:\n", test_df)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37

# Output rslt_df:           roc1 0         NaN 1         NaN 2  138.420000 3  126.035232 4  -23.974330 5   44.209704 6    3.210206 7   14.789412 8   14.285714 dtypes=  roc1    float64 dtype: object rslt_df:             roc1 0           NaN 1           NaN 2  11111.000000 3    126.035232 4    -23.974330 5     44.209704 6      3.210206 7     14.789412 8     14.285714 dtypes2=  Series([], dtype: object) test_df:            Close 0  33333.000000 1      2.384200 2      5.389132 3      4.097124 4      5.908450 5      6.098123 6      7.000000 7      8.000000 8  44444.000000

* Update: I added two prints of the dtypes of each DataFrame and the one that is working is a Series[] and the one that is not working is a float64. Not sure if this is the problem and I don't know why the two are different but it feels like a little progress ;-/

**Update: I had a slight error in the code where I was not assigning rslt_df to the new replaced rslt_df, so I thought that was the issue but it does not work if I add floats into the picture. Notice the new output works when the value is a float that does not have too many decimals - so that has to be the issue! Ex: 138.420000 gets repalced but 126.035232 and 44.209704 do not.

[bcrypto]

Found the solution!

rslt_df = rslt_df.round(6) cleans up a rounding issues I believe and now it works:

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35

import pandas as pd import talib as ta import numpy as np   rslt_df = pd.DataFrame() test_df = pd.DataFrame()   df = pd.DataFrame({'Close': [1, 2.3842, 5.389132, 4.09712373, 5.90845, 6.0981234, 7, 8, 9]})   # ROC------------------------------------------------- for i in range(2):     if i != 0:         rslt_df['roc%i' % i] = ta.ROC(df['Close'].shift(1), timeperiod=i)   # replace rtest_df print("rslt_df:\n", rslt_df) print("dtypes= ", rslt_df.dtypes) rslt_df = rslt_df.fillna(0)   # rslt_df = rslt_df.roc1.astype(int) rslt_df = rslt_df.round(6)  # *****SOLUTION*****     rslt_df = rslt_df.replace(to_replace=[138.42, 126.035232, 44.209704], value=[11111, 22222, 33333])  # This one does NOT replace   # rslt_df = rslt_df.mask(np.isclose(df.values, 126.035232))     print("rslt_df:\n", rslt_df)   # replace roc df--------------------- print("dtypes2= ", test_df.dtypes) test_df = df.replace(to_replace=[1, 9], value=[33333, 44444])   print("test_df:\n", test_df)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37

# OUTPUT rslt_df:           roc1 0         NaN 1         NaN 2  138.420000 3  126.035232 4  -23.974330 5   44.209704 6    3.210206 7   14.789412 8   14.285714 dtypes=  roc1    float64 dtype: object rslt_df:             roc1 0      0.000000 1      0.000000 2  11111.000000 3  22222.000000 4    -23.974330 5  33333.000000 6      3.210206 7     14.789412 8     14.285714 dtypes2=  Series([], dtype: object) test_df:            Close 0  33333.000000 1      2.384200 2      5.389132 3      4.097124 4      5.908450 5      6.098123 6      7.000000 7      8.000000 8  44444.000000