将具有恒定值的列添加到pandas数据框 [英] Add column with constant value to pandas dataframe

问题描述

给出一个数据框:

np.random.seed(0)
df = pd.DataFrame(np.random.randn(3, 3), columns=list('ABC'), index=[1, 2, 3])
df

          A         B         C
1  1.764052  0.400157  0.978738
2  2.240893  1.867558 -0.977278
3  0.950088 -0.151357 -0.103219

添加包含常量值(例如0)的新列的最简单方法是什么?

What is the simplest way to add a new column containing a constant value eg 0?

          A         B         C  new
1  1.764052  0.400157  0.978738    0
2  2.240893  1.867558 -0.977278    0
3  0.950088 -0.151357 -0.103219    0

---

这是我的解决方案，但我不知道为什么这会将NaN放入新"列?

---

This is my solution, but I don't know why this puts NaN into 'new' column?

df['new'] = pd.Series([0 for x in range(len(df.index))])

          A         B         C  new
1  1.764052  0.400157  0.978738  0.0
2  2.240893  1.867558 -0.977278  0.0
3  0.950088 -0.151357 -0.103219  NaN

推荐答案

之所以将NaN放入列中，是因为右侧对象的df.index和Index不同. @zach显示了分配新的零列的正确方法.通常，pandas尝试尽可能多地对齐索引.缺点之一是，当索引未对齐时，无论没有对齐，都会得到NaN.尝试使用reindex和align方法来获得一些直觉，以便对具有部分，完全和未对齐所有对齐索引的对象进行对齐.例如，以下是DataFrame.align()与部分对齐的索引一起工作的方式:

The reason this puts NaN into a column is because df.index and the Index of your right-hand-side object are different. @zach shows the proper way to assign a new column of zeros. In general, pandas tries to do as much alignment of indices as possible. One downside is that when indices are not aligned you get NaN wherever they aren't aligned. Play around with the reindex and align methods to gain some intuition for alignment works with objects that have partially, totally, and not-aligned-all aligned indices. For example here's how DataFrame.align() works with partially aligned indices:

In [7]: from pandas import DataFrame

In [8]: from numpy.random import randint

In [9]: df = DataFrame({'a': randint(3, size=10)})

In [10]:

In [10]: df
Out[10]:
   a
0  0
1  2
2  0
3  1
4  0
5  0
6  0
7  0
8  0
9  0

In [11]: s = df.a[:5]

In [12]: dfa, sa = df.align(s, axis=0)

In [13]: dfa
Out[13]:
   a
0  0
1  2
2  0
3  1
4  0
5  0
6  0
7  0
8  0
9  0

In [14]: sa
Out[14]:
0     0
1     2
2     0
3     1
4     0
5   NaN
6   NaN
7   NaN
8   NaN
9   NaN
Name: a, dtype: float64

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