# add column with constant value to pandas dataframe

I don't know why this puts NaN into 'new' column?

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

EDIT:

```
df['new'] = 0
```

works :)

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
```

From: stackoverflow.com/q/24039023

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