从 pandas 数据框的列或行中获取列表? [英] Get list from pandas dataframe column or row?

问题描述

我有一个从Excel文档中导入的数据框 df ，如下所示:

I have a dataframe df imported from an Excel document like this:

cluster load_date   budget  actual  fixed_price
A   1/1/2014    1000    4000    Y
A   2/1/2014    12000   10000   Y
A   3/1/2014    36000   2000    Y
B   4/1/2014    15000   10000   N
B   4/1/2014    12000   11500   N
B   4/1/2014    90000   11000   N
C   7/1/2014    22000   18000   N
C   8/1/2014    30000   28960   N
C   9/1/2014    53000   51200   N

我希望能够将第1列 df ['cluster'] 的内容作为列表返回，因此我可以对其进行循环操作，并为每个列表创建一个Excel工作表集群.

I want to be able to return the contents of column 1 df['cluster'] as a list, so I can run a for-loop over it, and create an Excel worksheet for every cluster.

是否还可以将整个列或行的内容返回到列表?例如

Is it also possible to return the contents of a whole column or row to a list? e.g.

list = [], list[column1] or list[df.ix(row1)]

推荐答案

Pandas DataFrame列在拔出时即为Pandas系列，然后可以调用 x.tolist()来打开它们.进入Python列表.或者，您可以使用 list(x)投射它.

Pandas DataFrame columns are Pandas Series when you pull them out, which you can then call x.tolist() on to turn them into a Python list. Alternatively you cast it with list(x).

import pandas as pd

data_dict = {'one': pd.Series([1, 2, 3], index=['a', 'b', 'c']),
             'two': pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])}

df = pd.DataFrame(data_dict)

print(f"DataFrame:\n{df}\n")
print(f"column types:\n{df.dtypes}")

col_one_list = df['one'].tolist()

col_one_arr = df['one'].to_numpy()

print(f"\ncol_one_list:\n{col_one_list}\ntype:{type(col_one_list)}")
print(f"\ncol_one_arr:\n{col_one_arr}\ntype:{type(col_one_arr)}")

输出:

DataFrame:
   one  two
a  1.0    1
b  2.0    2
c  3.0    3
d  NaN    4

column types:
one    float64
two      int64
dtype: object

col_one_list:
[1.0, 2.0, 3.0, nan]
type:<class 'list'>

col_one_arr:
[ 1.  2.  3. nan]
type:<class 'numpy.ndarray'>

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