What is the difference between head(10) and [:10] in pyhton?

I am newbie. Instructor said ı am going to select first ten then he write the code below.

df['Runtime'].value_counts().sort_values(ascending = False)[:10]

I tried the code df['Runtime'].value_counts().sort_values(ascending = False)[:10] it gives me different result. What's the fundamental difference between the two different expressions?

df['Runtime'].value_counts().sort_values(ascending = False)[:10]
90.0    971
95.0    489
92.0    434
93.0    422
85.0    408
       ... 
19.0      8
32.0      8
9.0       8
7.0       8
10.0      8
Name: Runtime, Length: 157, dtype: int64
.
df['Runtime'].value_counts().sort_values(ascending = False).head(10)
90.0     971
95.0     489
92.0     434
93.0     422
85.0     408
89.0     407
88.0     406
100.0    402
91.0     394
94.0     383
Name: Runtime, dtype: int64

Why the surprising results?

Because the Runtime column in your DataFrame is of float datatype, the result of value_counts() is a Series with an index of type Float64Index.

The behaviour of [ ] style indexing on a series with a Float64Index is quirky. See this GitHub issue discussion:

> To summarize in a specific way [...] for all index types, using integers in [] (__getitem__) is positional (like iloc), except for Float64Index, making this a special case.

Essentially, with a float index, [:10] is interpreted not as "the first ten values" but as "all the values until the one where the index value is 10.0".

With your data, that's why you see df['Runtime'].value_counts().sort_values(ascending = False)[:10] giving 157 rows, with the last row having an index value of 10.0.

Getting the behaviour you want

To get the first ten values like you want, you can do:

df['Runtime'].value_counts().sort_values(ascending = False).iloc[:10]

or equivalently:

df['Runtime'].value_counts().sort_values(ascending = False).head(10)

(Looking at the source code shows that .head(n) returns the result of .iloc[:n].)

How to reproduce

We don't have access to your data, so here's a quick script I made to produce a similar enough DataFrame to reproduce the issue.

import numpy as np
import pandas as pd
np.random.seed(5678)
vals = np.random.randint(0, 50, size=1000).astype(float)
df = pd.DataFrame(vals, columns=['Runtime'])
s = df['Runtime'].value_counts().sort_values(ascending = False)
s[:10]   # produces 32 rows, the last one with index 10.0
s.iloc[:10]  # the first 10 rows
s.head(10)   # also the first 10 rows