Python：从现有列创建一个新列
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Python: create a new column from existing columns
提问by Kexin Xu
I am trying to create a new column based on both columns. Say I want to create a new column z, and it should be the value of y when it is not missing and be the value of x when y is indeed missing. So in this case, I expect z to be [1, 8, 10, 8].
我正在尝试基于两列创建一个新列。假设我想创建一个新列 z，它应该是 y 没有缺失时的值，当 y 确实缺失时它应该是 x 的值。所以在这种情况下，我希望 z 是[1, 8, 10, 8]。
x y
0 1 NaN
1 2 8
2 4 10
3 8 NaN
采纳答案by Vidhya G
The new column 'z'get its values from column 'y'using df['z'] = df['y']. This brings over the missing values so fill them in using fillnausing column 'x'. Chain these two actions:
新'z'列'y'使用df['z'] = df['y']. 这会带来缺失值，因此使用fillnausing column填充它们'x'。将这两个动作串联起来：
>>> df['z'] = df['y'].fillna(df['x'])
>>> df
x y z
0 1 NaN 1
1 2 8 8
2 4 10 10
3 8 NaN 8
回答by Red Twoon
I'm not sure if I understand the question, but would this be what you're looking for?
我不确定我是否理解这个问题，但这会是你要找的吗？
"if y[i]" will skip if the value is none.
如果值为 none，“if y[i]”将跳过。
for i in range(len(x));
if y[i]:
z.append(y[i])
else:
z.append(x[i])
回答by Kyler Brown
Let's say DataFrame is called df. First copy the ycolumn.
假设 DataFrame 被调用df。首先复制y列。
df["z"] = df["y"].copy()
Then set the nan locations of z to the locations in x where the nans are in z.
然后将 z 的 nan 位置设置为 x 中 nan 在 z 中的位置。
import numpy as np
df.z[np.isnan(df.z)]=df.x[np.isnan(df.z)]
>>> df
x y z
0 1 NaN 1
1 2 8 8
2 4 10 10
3 8 NaN 8
回答by EdChum
Use np.where:
使用np.where：
In [3]:
df['z'] = np.where(df['y'].isnull(), df['x'], df['y'])
df
Out[3]:
x y z
0 1 NaN 1
1 2 8 8
2 4 10 10
3 8 NaN 8
Here it uses the boolean condition and if true returns df['x']else df['y']
这里它使用布尔条件，如果为真则返回df['x']其他df['y']
回答by Haleemur Ali
You can use applywith option axis=1. Then your solution is pretty concise.
您可以apply与选项一起使用axis=1。那么您的解决方案非常简洁。
df[z] = df.apply(lambda row: row.y if pd.notnull(row.y) else row.x, axis=1)
回答by ari
The updatemethod does almost exactly this. The only caveat is that updatewill do so in place so you must first create a copy:
该update方法几乎就是这样做的。唯一需要注意的是，这update将就地进行，因此您必须先创建一个副本：
df['z'] = df.x.copy()
df.z.update(df.y)
In the above example you start with xand replace each value with the corresponding value from y, as long as the new value is not NaN.
在上面的示例中，只要新值不是 ，您就可以从 开始x并将每个值替换y为来自 的相应值NaN。
