pandas change values on multiple column based on condition

Question

I have a data frame like

    x           y           w           h
0   1593.826218 1293.189452 353.268389  74.493565
1   1680.089430 1956.536916 87.632469   42.567752
2   1362.421731 1908.648195 52.031778   42.567752
3   1599.303248 1385.419580 351.899131  78.040878
4   1500.716721 1121.144789 397.084623  46.115064
5   1513.040037 1186.770072 514.840753  86.909160
6   1387.068363 1804.002472 212.234885  44.341408
7   787.333657  379.756446  416.254225  70.946253

I want to select rows based on certain value ranges in x and y and find the values in all four x,y,w,h and perform addition or subtraction on those values and replace them with the calculated value in that row.

I am doing something like

df.loc[(df['x'] >= 1000) & (df['x'] < 1800) & (df['y'] >= 1150)  & (df['y'] < 1290), ['x', 'y', 'w','h']] =  df['x'] - 20, df['y'] - 165, df['w'] + 26, df['h'] - 29

and getting error: "Must have equal len keys and value when setting with an ndarray"

when I tried this

df.loc[(df['x'] >= 1000) & (df['x'] < 1800) & (df['y'] >= 1150)  & (df['y'] < 1290), 'x'] =  df['x'] - 20

it works but I want to perform operation on all four columns in one go and update the values.

My desired answer is it should select row 5 and my answer should be like

    x           y           w           h
5   1493.040037 1021.770072 540.840753  57.909160

Any help will be much appreciated.

Shubham Sharma · Accepted Answer · 2021-09-04 17:46:24Z

5

Let us fix your code

m = (df['x'] >= 1000) & (df['x'] < 1800) \
        & (df['y'] >= 1150)  & (df['y'] < 1290)

df.loc[m] += [-20, -165, 26, -29]

             x            y           w          h
0  1593.826218  1293.189452  353.268389  74.493565
1  1680.089430  1956.536916   87.632469  42.567752
2  1362.421731  1908.648195   52.031778  42.567752
3  1599.303248  1385.419580  351.899131  78.040878
4  1500.716721  1121.144789  397.084623  46.115064
5  1493.040037  1021.770072  540.840753  57.909160 *** updated
6  1387.068363  1804.002472  212.234885  44.341408
7   787.333657   379.756446  416.254225  70.946253

answered Sep 4, 2021 at 17:46

Shubham Sharma

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Comments

anky · Accepted Answer · 2021-09-04 17:45:48Z

2

With your approach , you can use pd.concat on the R.H.S

df.loc[(df['x'] >= 1000) & (df['x'] < 1800) & (df['y'] >= 1150)  & (df['y'] < 1290), ['x', 'y', 'w','h']]=pd.concat((df['x'] - 20, df['y'] - 165, df['w'] + 26, df['h'] - 29),axis=1)

             x            y           w          h
0  1593.826218  1293.189452  353.268389  74.493565
1  1680.089430  1956.536916   87.632469  42.567752
2  1362.421731  1908.648195   52.031778  42.567752
3  1599.303248  1385.419580  351.899131  78.040878
4  1500.716721  1121.144789  397.084623  46.115064
5  1493.040037  1021.770072  540.840753  57.909160
6  1387.068363  1804.002472  212.234885  44.341408
7   787.333657   379.756446  416.254225  70.946253

answered Sep 4, 2021 at 17:45

anky

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Comments

Henry Yik · Accepted Answer · 2021-09-04 17:46:08Z

1

You have to assign with an array of the same shape. Easiest way is to use the original df:

m = (df['x'] >= 1000) & (df['x'] < 1800) & (df['y'] >= 1150) & (df['y'] < 1290)

df.loc[m] = df.assign(x=df["x"]-20, y=df["y"]-165, w=df['w']+26, h=df['h']-29)

print (df[m])

             x            y           w         h
5  1493.040037  1021.770072  540.840753  57.90916

answered Sep 4, 2021 at 17:46

Henry Yik

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