在Python大 pandas 中，从1开始行索引而不是零，而不创建其他列 [英] In Python pandas, start row index from 1 instead of zero without creating additional column

问题描述

我知道我可以重新设置这样的索引

I know that I can reset the indices like so

df.reset_index(inplace=True)

但这将从 0 开始索引。我想从 1 开始。如何创建任何额外的列，并保持index / reset_index的功能和选项？我不想想要创建一个新的数据框，所以 inplace = True 应该仍然适用。

but this will start the index from 0. I want to start it from 1. How do I do that without creating any extra columns and by keeping the index/reset_index functionality and options? I do not want to create a new dataframe, so inplace=True should still apply.

推荐答案

直接分配一个新的索引数组：

Just assign directly a new index array:

df.index = np.arange(1, len(df) + 1)

示例：

In [151]:

df = pd.DataFrame({'a':np.random.randn(5)})
df
Out[151]:
          a
0  0.443638
1  0.037882
2 -0.210275
3 -0.344092
4  0.997045
In [152]:

df.index = np.arange(1,len(df)+1)
df
Out[152]:
          a
1  0.443638
2  0.037882
3 -0.210275
4 -0.344092
5  0.997045

或者只是：

df.index = df.index + 1

如果索引已经为0，则

时间

由于某种原因，我无法在 reset_index 以下是100,000行df的时间：

For some reason I can't take timings on reset_index but the following are timings on a 100,000 row df:

In [160]:

%timeit df.index = df.index + 1
The slowest run took 6.45 times longer than the fastest. This could mean that an intermediate result is being cached 
10000 loops, best of 3: 107 µs per loop


In [161]:

%timeit df.index = np.arange(1, len(df) + 1)
10000 loops, best of 3: 154 µs per loop

所以没有时间为 reset_index 我不能说明确，但是它似乎只是为每个索引值添加1如果索引已经 0 基于

So without the timing for reset_index I can't say definitively, however it looks like just adding 1 to each index value will be faster if the index is already 0 based

这篇关于在Python大 pandas 中，从1开始行索引而不是零，而不创建其他列的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！