Stack Overflow Asked by Neyls on October 27, 2020
From the following dataframe:
dim_0 dim_1
0 0 40.54 23.40 6.70 1.70 1.82 0.96 1.62
1 175.89 20.24 7.78 1.55 1.45 0.80 1.44
2 0.00 0.00 0.00 0.00 0.00 0.00 0.00
1 0 21.38 24.00 5.90 1.60 2.55 1.50 2.36
1 130.29 18.40 8.49 1.52 1.45 0.80 1.47
2 0.00 0.00 0.00 0.00 0.00 0.00 0.00
2 0 6.30 25.70 5.60 1.70 2.16 1.16 1.87
1 73.45 21.49 6.88 1.61 1.61 0.94 1.63
2 0.00 0.00 0.00 0.00 0.00 0.00 0.00
3 0 16.64 25.70 5.70 1.60 2.17 1.12 1.76
1 125.89 19.10 7.52 1.43 1.44 0.78 1.40
2 0.00 0.00 0.00 0.00 0.00 0.00 0.00
4 0 41.38 24.70 5.60 1.50 2.08 1.16 1.85
1 0.00 0.00 0.00 0.00 0.00 0.00 0.00
2 0.00 0.00 0.00 0.00 0.00 0.00 0.00
5 0 180.59 16.40 3.80 1.10 4.63 3.86 5.71
1 0.00 0.00 0.00 0.00 0.00 0.00 0.00
2 0.00 0.00 0.00 0.00 0.00 0.00 0.00
6 0 13.59 24.40 6.10 1.70 2.62 1.51 2.36
1 103.19 19.02 8.70 1.53 1.48 0.76 1.38
2 0.00 0.00 0.00 0.00 0.00 0.00 0.00
7 0 3.15 24.70 5.60 1.50 2.14 1.22 2.00
1 55.90 23.10 6.07 1.50 1.86 1.12 1.87
2 208.04 20.39 6.82 1.35 1.47 0.95 1.67
How can I get only the rows from dim_01 that match the array [1 0 0 1 2 0 1 2]?
Desired result is:
0 175.89 20.24 7.78 1.55 1.45 0.80 1.44
1 21.38 24.00 5.90 1.60 2.55 1.50 2.36
2 6.30 25.70 5.60 1.70 2.16 1.16 1.87
3 125.89 19.10 7.52 1.43 1.44 0.78 1.40
4 0.00 0.00 0.00 0.00 0.00 0.00 0.00
5 180.59 16.40 3.80 1.10 4.63 3.86 5.71
7 103.19 19.02 8.70 1.53 1.48 0.76 1.38
8 208.04 20.39 6.82 1.35 1.47 0.95 1.67
I’ve tried using slicing, cross-section, etc but no success.
Thanks in advance for the help.
Use MultiIndex.from_arrays and select by DataFrame.loc:
arr = np.array([1, 0, 0, 1, 2, 0, 1 ,2])
df = df.loc[pd.MultiIndex.from_arrays([df.index.levels[0], arr])]
print (df)
2 3 4 5 6 7 8
0
0 1 175.89 20.24 7.78 1.55 1.45 0.80 1.44
1 0 21.38 24.00 5.90 1.60 2.55 1.50 2.36
2 0 6.30 25.70 5.60 1.70 2.16 1.16 1.87
3 1 125.89 19.10 7.52 1.43 1.44 0.78 1.40
4 2 0.00 0.00 0.00 0.00 0.00 0.00 0.00
5 0 180.59 16.40 3.80 1.10 4.63 3.86 5.71
6 1 103.19 19.02 8.70 1.53 1.48 0.76 1.38
7 2 208.04 20.39 6.82 1.35 1.47 0.95 1.67
arr = np.array([1, 0, 0, 1, 2, 0, 1 ,2])
df = df.loc[pd.MultiIndex.from_arrays([df.index.levels[0], arr])].droplevel(1)
print (df)
2 3 4 5 6 7 8
0
0 175.89 20.24 7.78 1.55 1.45 0.80 1.44
1 21.38 24.00 5.90 1.60 2.55 1.50 2.36
2 6.30 25.70 5.60 1.70 2.16 1.16 1.87
3 125.89 19.10 7.52 1.43 1.44 0.78 1.40
4 0.00 0.00 0.00 0.00 0.00 0.00 0.00
5 180.59 16.40 3.80 1.10 4.63 3.86 5.71
6 103.19 19.02 8.70 1.53 1.48 0.76 1.38
7 208.04 20.39 6.82 1.35 1.47 0.95 1.67
Correct answer by jezrael on October 27, 2020
Try the following code:
mask_array = [1 0 0 1 2 0 1 2]
df_first = pd.DataFrame() # < It's your first array >
new_array = df_first[df_first['dim_1'].isin(mask_array)]
Answered by Roman_N on October 27, 2020
I'd go with advanced indexing using Numpy:
l = [1, 0, 0, 1, 2, 0, 1, 2]
i,j = df.index.levels
ix = np.array(l)+np.arange(i.max()+1)*(j.max()+1)
pd.DataFrame(df.to_numpy()[ix])
0 1 2 3 4 5 6
0 175.89 20.24 7.78 1.55 1.45 0.80 1.44
1 21.38 24.00 5.90 1.60 2.55 1.50 2.36
2 6.30 25.70 5.60 1.70 2.16 1.16 1.87
3 125.89 19.10 7.52 1.43 1.44 0.78 1.40
4 0.00 0.00 0.00 0.00 0.00 0.00 0.00
5 180.59 16.40 3.80 1.10 4.63 3.86 5.71
6 103.19 19.02 8.70 1.53 1.48 0.76 1.38
7 208.04 20.39 6.82 1.35 1.47 0.95 1.67
Answered by yatu on October 27, 2020
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