I have a data frame. i want to bin values and append it to the new column. i can do it with pd.cut. But the thing is that, with pd.cut i set labels and bins manually. But, i want to just set step size (not bin number). i tried also np.linespace, np.arange but there i have to specify the start and end point also bin count. but there can be a dataframe which i would not be able to know the max and min number in dataframe
df = pd.DataFrame([10, 10, 23, 42, 51, 33, 52, 42,44, 67, 65, 12, 10, 2, 3, 2, 77, 76],columns=['values'])
bins = [0, 10, 20,30, 40, 50, 60, 70]
labels = ['0-10', '10-20', '20-30', '30-40', '40-50', '50-60', '60-70']
df['bins'] = pd.cut(df['values'], bins, labels=labels)
print (df)
values bins
0 10 0-10
1 10 0-10
2 23 20-30
3 42 40-50
4 51 50-60
5 33 30-40
6 52 50-60
7 42 40-50
8 44 40-50
9 67 60-70
10 65 60-70
11 12 10-20
12 10 0-10
13 2 0-10
14 3 0-10
15 2 0-10
16 77 NaN
17 76 NaN
Here is my output, i want to get same output but not with manually setting bins and labels p.s. As u see from here if i have the values greater than 70 it will be Nan. So it is also reason why i want to just set step size "10". I can have continues values, so i want it to label is automatically by using steps size 10
I would really aprreciate your helps
Thanks!!!
Just a slight variation to your code, note that I added a row with value 93 at the end of your df.
df = pd.DataFrame([10, 10, 23, 42, 51, 33, 52, 42,44, 67, 65, 12, 10, 2, 3, 2, 77, 76, 93],columns=['values'])
bins = np.arange(0,df['values'].max() + 10, 10)
df['bins'] = pd.cut(df['values'], bins)
values bins
0 10 (0, 10]
1 10 (0, 10]
2 23 (20, 30]
3 42 (40, 50]
4 51 (50, 60]
5 33 (30, 40]
6 52 (50, 60]
7 42 (40, 50]
8 44 (40, 50]
9 67 (60, 70]
10 65 (60, 70]
11 12 (10, 20]
12 10 (0, 10]
13 2 (0, 10]
14 3 (0, 10]
15 2 (0, 10]
16 77 (70, 80]
17 76 (70, 80]
18 93 (90, 100]
Edit: To include zeros in the bins as asked in the comments, set the parameter include_lowest to True
df = pd.DataFrame([0, 0, 0, 10, 10, 23, 42, 51, 33, 52, 42,44, 67, 65, 12, 10, 2, 3, 2, 77, 76, 93],columns=['values'])
bins = np.arange(0,df['values'].max() + 10, 10)
df['bins'] = pd.cut(df['values'], bins, include_lowest=True)
You get
values bins
0 0 (-0.001, 10.0]
1 0 (-0.001, 10.0]
2 0 (-0.001, 10.0]
3 10 (-0.001, 10.0]
4 10 (-0.001, 10.0]
5 23 (20.0, 30.0]
6 42 (40.0, 50.0]
7 51 (50.0, 60.0]
8 33 (30.0, 40.0]
9 52 (50.0, 60.0]
10 42 (40.0, 50.0]
11 44 (40.0, 50.0]
12 67 (60.0, 70.0]
13 65 (60.0, 70.0]
14 12 (10.0, 20.0]
15 10 (-0.001, 10.0]
16 2 (-0.001, 10.0]
17 3 (-0.001, 10.0]
18 2 (-0.001, 10.0]
19 77 (70.0, 80.0]
20 76 (70.0, 80.0]
21 93 (90.0, 100.0]