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?
当前回答
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)
其他回答
如果你只需要创建一个新的空列，那么最短的解决方案是:
df.loc[:, 'e'] = pd.Series()
我正在寻找一种添加numpy列的通用方法。nans到一个数据帧而不得到愚蠢的SettingWithCopyWarning。
从以下方面:
答案在这里 关于将变量作为关键字参数传递的问题 此方法用于生成一个numpy数组的NaNs
我想到了这个:
col = 'column_name'
df = df.assign(**{col:numpy.full(len(df), numpy.nan)})
import pandas as pd
# Define a dictionary containing data
data = {'a': [0,0,0.671399,0.446172,0,0.614758],
'b': [0,0,0.101208,-0.243316,0,0.075793],
'c': [0,0,-0.181532,0.051767,0,-0.451460],
'd': [0,0,0.241273,1.577318,0,-0.012493]}
# Convert the dictionary into DataFrame
df = pd.DataFrame(data)
# Declare a list that is to be converted into a column
col_e = [-0.335485,-1.166658,-0.385571,0,0,0]
df['e'] = col_e
# add column 'e'
df['e'] = col_e
# Observe the result
df
如果你要添加的列是一个序列变量，那么只需:
df["new_columns_name"]=series_variable_name #this will do it for you
即使要替换现有列，这种方法也很有效。只需输入与要替换的列相同的new_columns_name。它只会用新的系列数据覆盖现有的列数据。
我得到了可怕的SettingWithCopyWarning，它没有通过使用iloc语法修复。我的DataFrame是由read_sql从ODBC源创建的。根据上面low - tech的建议，以下方法对我来说是有效的:
df.insert(len(df.columns), 'e', pd.Series(np.random.randn(sLength), index=df.index))
This worked fine to insert the column at the end. I don't know if it is the most efficient, but I don't like warning messages. I think there is a better solution, but I can't find it, and I think it depends on some aspect of the index. Note. That this only works once and will give an error message if trying to overwrite and existing column. Note As above and from 0.16.0 assign is the best solution. See documentation http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.assign.html#pandas.DataFrame.assign Works well for data flow type where you don't overwrite your intermediate values.
