英文:
What is the difference between head(10) and [:10] in pyhton?
问题
df['Runtime'].value_counts().sort_values(ascending = False)[:10]
我是新手。导师说我将选择前十个，然后他写了以下代码。
我尝试了这段代码 df['Runtime'].value_counts().sort_values(ascending = False)[:10] 它给了我不同的结果。这两个不同表达式之间的根本区别是什么？
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
英文:
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
答案1
得分: 1
因为您的DataFrame中的Runtime列是float数据类型，所以value_counts()的结果是一个带有Float64Index类型索引的Series。
在具有Float64Index的Series上执行[ ]样式的索引行为是古怪的。请参阅这个GitHub问题讨论：
总结一下，在所有索引类型中，使用整数在
[]（__getitem__）中是位置索引（类似于iloc），但对于Float64Index，这是一个特殊情况。
基本上，使用浮点索引，[:10]的解释不是“前十个值”，而是“直到索引值为10.0的所有值”。
因此，根据您的数据，这就是为什么您会看到df['Runtime'].value_counts().sort_values(ascending = False)[:10]返回157行，最后一行的索引值为10.0。
要获得您想要的前十个值，可以执行以下操作：
df['Runtime'].value_counts().sort_values(ascending = False).iloc[:10]
或者等效地：
df['Runtime'].value_counts().sort_values(ascending = False).head(10)
查看源代码显示.head(n)返回.iloc[:n]的结果。
如何重现：
我们无法访问您的数据，因此这是一个我创建的快速脚本，用于生成一个足够相似的DataFrame以重现此问题。
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] # 产生32行，最后一行索引为10.0
s.iloc[:10] # 前10行
s.head(10) # 也是前10行
英文:
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
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