Newbie question: Why does this indexing work? [duplicate] - pandas

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This question already has an answer here:
Why does .loc have inclusive behavior for slices?
(1 answer)
Closed 3 years ago.
I am trying to extract just the verisicolor petal length entries in the iris dataset of scikit learn. This corresponds to rows 50 to 99. I have always been told that python indexing excludes the final entry, i.e. 1:10 is all the numbers from 1 to 9.
So, why is it that the following command includes row 99? Is this inclusive indexing (where the final value is included) just a pandas thing with loc? My code is below and it works, but I dont' know why, my intuition would have been to set the index in loc to [50:100]
from sklearn import datasets
import pandas as pd
import numpy as np
iris = datasets.load_iris() #load iris
iris_df=pd.DataFrame(data= np.c_[iris['data'], iris['target']],
columns= iris['feature_names'] + ['target']) #convert iris to dataframe
versicolor_petal_length=iris_df.loc[50:99,['petal length (cm)']] #extract rows 50-99 of petal length (cm) column
print(versicolor_petal_length)
The output includes row 99,
petal length (cm)
50 4.7
51 4.5
52 4.9
53 4.0
54 4.6
55 4.5
56 4.7
57 3.3
58 4.6
59 3.9
60 3.5
61 4.2
62 4.0
63 4.7
64 3.6
65 4.4
66 4.5
67 4.1
68 4.5
69 3.9
70 4.8
71 4.0
72 4.9
73 4.7
74 4.3
75 4.4
76 4.8
77 5.0
78 4.5
79 3.5
80 3.8
81 3.7
82 3.9
83 5.1
84 4.5
85 4.5
86 4.7
87 4.4
88 4.1
89 4.0
90 4.4
91 4.6
92 4.0
93 3.3
94 4.2
95 4.2
96 4.2
97 4.3
98 3.0
99 4.1
Given this, can someone explain to me when indexing is will include the last element and when indexing will exclude it? I am having some trouble with this.
Thanks

Answer 1

I believe you're thinking of np.arange which belongs to the Numpy library (excludes last index as seen here) whereas df.loc is from the Pandas library and is all inclusive w.r.t indexing as seen in examples here
EDIT to add: you might also be thinking of how for loops work in python regarding the range functionality. When it comes to indexing and playing with new libraries it never hurts to double check some documentation :)
if you have any further questions feel free to ask

Answer 2

what you are experiencing here is a DataFrame.loc[] property.
As mentioned in the documentation as a warning and i quote:
Warning : Note that contrary to usual python slices, both the start and the stop are included
Here is a link with an example provided from the pandas docs:
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.loc.html