iloc方法得到pandas序列求和是浮点数
重命名列，merge方法：
(base) xutengfei@xutengfei-G3-3579:~$ python
Python 3.8.5 (default, Sep 4 2020, 07:30:14)
[GCC 7.3.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> df1=pd.DataFrame(np.arange(9).reshape((3,3)),columns=list("hey"))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name 'np' is not defined
>>> import numpy as np
>>> df1=pd.DataFrame(np.arange(9).reshape((3,3)),columns=list("hey"))
>>> df1
h e y
0 0 1 2
1 3 4 5
2 6 7 8
>>> df2=pd.DataFrame(np.ones(2,4),index=['E','F'],columns=list("hold"))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/xutengfei/.local/lib/python3.8/site-packages/numpy/core/numeric.py", line 192, in ones
a = empty(shape, dtype, order)
TypeError: Cannot interpret '4' as a data type
>>> df2=pd.DataFrame(np.ones(2,4),index=['E','F'],columns=list("hold"))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/xutengfei/.local/lib/python3.8/site-packages/numpy/core/numeric.py", line 192, in ones
a = empty(shape, dtype, order)
TypeError: Cannot interpret '4' as a data type
>>> df2=pd.DataFrame(np.ones(2,4),index=['E','F'],columns=list("hold"))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/xutengfei/.local/lib/python3.8/site-packages/numpy/core/numeric.py", line 192, in ones
a = empty(shape, dtype, order)
TypeError: Cannot interpret '4' as a data type
>>> df2=pd.DataFrame(np.ones((2,4)),index=['E','F'],columns=list("hold"))
>>> df2
h o l d
E 1.0 1.0 1.0 1.0
F 1.0 1.0 1.0 1.0
>>> df2.merge(df1,on="h")
Empty DataFrame
Columns: [h, o, l, d, e, y]
Index: []
>>> df2
h o l d
E 1.0 1.0 1.0 1.0
F 1.0 1.0 1.0 1.0
>>> df2.rename({
'h':'e'},inplace=True)
>>> df2
h o l d
E 1.0 1.0 1.0 1.0
F 1.0 1.0 1.0 1.0
>>> df2.rename(columns={
'h':'e'},inplace=True)
File "<stdin>", line 1
df2.rename(columns={
'h':'e'},inplace=True)
^
IndentationError: unexpected indent
>>> df2.rename(columns={
'h':'e'},inplace=True)
>>> df2
e o l d
E 1.0 1.0 1.0 1.0
F 1.0 1.0 1.0 1.0
>>> df2.merge(df1,on="3")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/xutengfei/anaconda3/lib/python3.8/site-packages/pandas/core/frame.py", line 7946, in merge
return merge(
File "/home/xutengfei/anaconda3/lib/python3.8/site-packages/pandas/core/reshape/merge.py", line 74, in merge
op = _MergeOperation(
File "/home/xutengfei/anaconda3/lib/python3.8/site-packages/pandas/core/reshape/merge.py", line 652, in __init__
) = self._get_merge_keys()
File "/home/xutengfei/anaconda3/lib/python3.8/site-packages/pandas/core/reshape/merge.py", line 1005, in _get_merge_keys
right_keys.append(right._get_label_or_level_values(rk))
File "/home/xutengfei/anaconda3/lib/python3.8/site-packages/pandas/core/generic.py", line 1563, in _get_label_or_level_values
raise KeyError(key)
KeyError: '3'
>>> df2.merge(df1,on="e")
e o l d h y
0 1.0 1.0 1.0 1.0 0 2
1 1.0 1.0 1.0 1.0 0 2
>>>
利用字典和列表创建dataframe的方法：
1.直接传入一个字典-键值对（键：各个列，值：各个列的行值，以列表呈现）
2.传入一个列表，列表中的每个元素是字典，记录每行的内容。\
如何避免人为0的影响：
df[df==0]=np.nan
如此一来，在计算平均值时，就可以避免人为0的影响。