Iterate panda data frame and a list using for loop,if loop, and zip

Question

I have a list and panda data frame and I want to use for loop, if loop and zip on them to get a single value from the data frame based on the corresponding value in v.

list v

v=[3,2,1,0,4,0,0,1,2,4]

pandas data frame df

               1st          2nd          3rd          4th
  b1      0.498717     0.264786   0.00992303  0.000516895
  b2      0.427093    0.0990702   0.00107178  2.75326e-05
  b3      0.276645    0.0322039  0.000112341  1.60488e-06
  b4       0.14827   0.00928838  1.09752e-05   9.2808e-08
  b5     0.0975582   0.00440099  2.86551e-06  1.83807e-08
  b6     0.0302828    0.0006493  1.04099e-07  3.58615e-10
  b7     0.0211258  0.000372098  4.07256e-08  1.19155e-10
  b8    0.00833787  9.24801e-05   4.0522e-09  8.08719e-12
  b9      0.028685  0.000596652  9.02113e-08  3.03026e-10
  b10  0.000693003   2.7417e-06   1.4319e-11  1.22682e-14

I tried this way but it returns an empty data frame

Empty DataFrame
Columns: []
Index: []

n=[] #or pd.DataFrame() 
for ns in range(0, len(v)):
    for i,row in list(zip(v,df)):# df.row,.iterrows(),.index 
        print(row)
        if i ==1:
            n.append(row.iloc[ns]['1st'])
        elif i==2:
            n.append(row.iloc[ns]['2nd'])
        elif i==3:
            n.append(row.iloc[ns]['3rd'])
        elif i == 4:
            n.append(row.iloc[ns]['4th'])
        else:
            n.append(0)
    vs=n
print(vs)

The output i am looking for

vs=[0.00992303,0.0990702 ,0.276645,0,.......] # or pd.Dataframe

Answer 1

First dont loop in pandas DataFrame rows, if exist some another vectorized solutions:

You can use numpy indexing, but because non column with 0 for 0 values first add it to 2d array with np.hstack:

arr = np.hstack((np.zeros((len(df), 1)), df.to_numpy()))
print (arr)
[[0.00000e+00 4.98717e-01 2.64786e-01 9.92303e-03 5.16895e-04]
 [0.00000e+00 4.27093e-01 9.90702e-02 1.07178e-03 2.75326e-05]
 [0.00000e+00 2.76645e-01 3.22039e-02 1.12341e-04 1.60488e-06]
 [0.00000e+00 1.48270e-01 9.28838e-03 1.09752e-05 9.28080e-08]
 [0.00000e+00 9.75582e-02 4.40099e-03 2.86551e-06 1.83807e-08]
 [0.00000e+00 3.02828e-02 6.49300e-04 1.04099e-07 3.58615e-10]
 [0.00000e+00 2.11258e-02 3.72098e-04 4.07256e-08 1.19155e-10]
 [0.00000e+00 8.33787e-03 9.24801e-05 4.05220e-09 8.08719e-12]
 [0.00000e+00 2.86850e-02 5.96652e-04 9.02113e-08 3.03026e-10]
 [0.00000e+00 6.93003e-04 2.74170e-06 1.43190e-11 1.22682e-14]]

---

out = arr[np.arange(len(df)), v].tolist()
print (out)
[0.00992303, 0.0990702, 0.276645, 0.0, 1.83807e-08, 0.0, 0.0, 
 0.00833787, 0.0005966519999999999, 1.22682e-14]

Another idea is add only zeros first column by DataFrame.insert, change columns names by range and then use DataFrame.lookup:

df.insert(0,'zero',0)
df.columns = range(len(df.columns))
print (df)
     0         1         2             3             4
b1   0  0.498717  0.264786  9.923030e-03  5.168950e-04
b2   0  0.427093  0.099070  1.071780e-03  2.753260e-05
b3   0  0.276645  0.032204  1.123410e-04  1.604880e-06
b4   0  0.148270  0.009288  1.097520e-05  9.280800e-08
b5   0  0.097558  0.004401  2.865510e-06  1.838070e-08
b6   0  0.030283  0.000649  1.040990e-07  3.586150e-10
b7   0  0.021126  0.000372  4.072560e-08  1.191550e-10
b8   0  0.008338  0.000092  4.052200e-09  8.087190e-12
b9   0  0.028685  0.000597  9.021130e-08  3.030260e-10
b10  0  0.000693  0.000003  1.431900e-11  1.226820e-14

out = df.lookup(df.index, v).tolist()
print (out)
[0.00992303, 0.0990702, 0.276645, 0.0, 1.83807e-08, 0.0, 0.0, 
 0.00833787, 0.0005966519999999999, 1.22682e-14]

Similar idea, only output is in new variable df1 if necessary not change original DataFrame:

df1 = (df.set_index(np.zeros(len(df)))
         .reset_index()
         .set_axis(np.arange(len(df.columns) + 1), inplace=False, axis=1))
print (df1)
     0         1         2             3             4
0  0.0  0.498717  0.264786  9.923030e-03  5.168950e-04
1  0.0  0.427093  0.099070  1.071780e-03  2.753260e-05
2  0.0  0.276645  0.032204  1.123410e-04  1.604880e-06
3  0.0  0.148270  0.009288  1.097520e-05  9.280800e-08
4  0.0  0.097558  0.004401  2.865510e-06  1.838070e-08
5  0.0  0.030283  0.000649  1.040990e-07  3.586150e-10
6  0.0  0.021126  0.000372  4.072560e-08  1.191550e-10
7  0.0  0.008338  0.000092  4.052200e-09  8.087190e-12
8  0.0  0.028685  0.000597  9.021130e-08  3.030260e-10
9  0.0  0.000693  0.000003  1.431900e-11  1.226820e-14

out = df1.lookup(df1.index, v).tolist()
print (out)
[0.00992303, 0.0990702, 0.276645, 0.0, 1.83807e-08, 0.0, 0.0, 
 0.00833787, 0.0005966519999999999, 1.22682e-14]