我有以下索引DataFrame命名列和行不连续的数字:
a b c d
2 0.671399 0.101208 -0.181532 0.241273
3 0.446172 -0.243316 0.051767 1.577318
5 0.614758 0.075793 -0.451460 -0.012493
我想添加一个新列,'e',到现有的数据帧,并不想改变数据帧中的任何东西(即,新列始终具有与DataFrame相同的长度)。
0 -0.335485
1 -1.166658
2 -0.385571
dtype: float64
如何将列e添加到上面的例子中?
你可以像这样通过for循环插入新列:
for label,row in your_dframe.iterrows():
your_dframe.loc[label,"new_column_length"]=len(row["any_of_column_in_your_dframe"])
示例代码如下:
import pandas as pd
data = {
"any_of_column_in_your_dframe" : ["ersingulbahar","yagiz","TS"],
"calories": [420, 380, 390],
"duration": [50, 40, 45]
}
#load data into a DataFrame object:
your_dframe = pd.DataFrame(data)
for label,row in your_dframe.iterrows():
your_dframe.loc[label,"new_column_length"]=len(row["any_of_column_in_your_dframe"])
print(your_dframe)
输出如下:
any_of_column_in_your_dframe |
calories |
duration |
new_column_length |
ersingulbahar |
420 |
50 |
13.0 |
yagiz |
380 |
40 |
5.0 |
TS |
390 |
45 |
2.0 |
你也可以这样用:
your_dframe["new_column_length"]=your_dframe["any_of_column_in_your_dframe"].apply(len)
你可以像这样通过for循环插入新列:
for label,row in your_dframe.iterrows():
your_dframe.loc[label,"new_column_length"]=len(row["any_of_column_in_your_dframe"])
示例代码如下:
import pandas as pd
data = {
"any_of_column_in_your_dframe" : ["ersingulbahar","yagiz","TS"],
"calories": [420, 380, 390],
"duration": [50, 40, 45]
}
#load data into a DataFrame object:
your_dframe = pd.DataFrame(data)
for label,row in your_dframe.iterrows():
your_dframe.loc[label,"new_column_length"]=len(row["any_of_column_in_your_dframe"])
print(your_dframe)
输出如下:
any_of_column_in_your_dframe |
calories |
duration |
new_column_length |
ersingulbahar |
420 |
50 |
13.0 |
yagiz |
380 |
40 |
5.0 |
TS |
390 |
45 |
2.0 |
你也可以这样用:
your_dframe["new_column_length"]=your_dframe["any_of_column_in_your_dframe"].apply(len)
为了完整起见-使用DataFrame.eval()方法的另一个解决方案:
数据:
In [44]: e
Out[44]:
0 1.225506
1 -1.033944
2 -0.498953
3 -0.373332
4 0.615030
5 -0.622436
dtype: float64
In [45]: df1
Out[45]:
a b c d
0 -0.634222 -0.103264 0.745069 0.801288
4 0.782387 -0.090279 0.757662 -0.602408
5 -0.117456 2.124496 1.057301 0.765466
7 0.767532 0.104304 -0.586850 1.051297
8 -0.103272 0.958334 1.163092 1.182315
9 -0.616254 0.296678 -0.112027 0.679112
解决方案:
In [46]: df1.eval("e = @e.values", inplace=True)
In [47]: df1
Out[47]:
a b c d e
0 -0.634222 -0.103264 0.745069 0.801288 1.225506
4 0.782387 -0.090279 0.757662 -0.602408 -1.033944
5 -0.117456 2.124496 1.057301 0.765466 -0.498953
7 0.767532 0.104304 -0.586850 1.051297 -0.373332
8 -0.103272 0.958334 1.163092 1.182315 0.615030
9 -0.616254 0.296678 -0.112027 0.679112 -0.622436
我想添加一个新的列,'e',到现有的数据帧,不改变数据帧中的任何东西。(序列总是与数据帧的长度相同。)
我假设e中的下标值与df1中的下标值匹配。
初始化一个名为e的新列,并将级数e中的值赋给它的最简单方法:
df['e'] = e.values
分配(熊猫0.16.0+)
从Pandas 0.16.0开始,你还可以使用assign,它将新列分配给DataFrame,并返回一个新对象(副本),其中包含所有原始列和新列。
df1 = df1.assign(e=e.values)
根据这个例子(也包括assign函数的源代码),你也可以包含多个列:
df = pd.DataFrame({'a': [1, 2], 'b': [3, 4]})
>>> df.assign(mean_a=df.a.mean(), mean_b=df.b.mean())
a b mean_a mean_b
0 1 3 1.5 3.5
1 2 4 1.5 3.5
在你的例子中:
np.random.seed(0)
df1 = pd.DataFrame(np.random.randn(10, 4), columns=['a', 'b', 'c', 'd'])
mask = df1.applymap(lambda x: x <-0.7)
df1 = df1[-mask.any(axis=1)]
sLength = len(df1['a'])
e = pd.Series(np.random.randn(sLength))
>>> df1
a b c d
0 1.764052 0.400157 0.978738 2.240893
2 -0.103219 0.410599 0.144044 1.454274
3 0.761038 0.121675 0.443863 0.333674
7 1.532779 1.469359 0.154947 0.378163
9 1.230291 1.202380 -0.387327 -0.302303
>>> e
0 -1.048553
1 -1.420018
2 -1.706270
3 1.950775
4 -0.509652
dtype: float64
df1 = df1.assign(e=e.values)
>>> df1
a b c d e
0 1.764052 0.400157 0.978738 2.240893 -1.048553
2 -0.103219 0.410599 0.144044 1.454274 -1.420018
3 0.761038 0.121675 0.443863 0.333674 -1.706270
7 1.532779 1.469359 0.154947 0.378163 1.950775
9 1.230291 1.202380 -0.387327 -0.302303 -0.509652
这个新特性首次引入时的描述可以在这里找到。