这里接着上一篇，继续记录下pandas中数据处理方面的函数
1. 重复数据
结果集中，可能会有重复数据，有函数可以做去重操作
#判断数据是否重复
DataFrame.duplicated(subset=None, keep='first')
Return boolean Series denoting duplicate rows, optionally only considering certain columns
#删除重复记录
DataFrame.drop_duplicates(subset=None, keep='first', inplace=False)
Return DataFrame with duplicate rows removed, optionally only considering certain columns
data = pd.DataFrame({'K1':['one']*3+['two']*4,'k2':[1,1,2,3,3,4,4]})
data
Out[100]:
K1 k2
0 one 1
1 one 1
2 one 2
3 two 3
4 two 3
5 two 4
6 two 4
#keep参数，默认是first，会以第一个为准，后面再出现就是True
data.duplicated()
Out[103]:
0 False
1 True
2 False
3 False
4 True
5 False
6 True
dtype: bool
data.duplicated(keep='last')
Out[116]:
0 True
1 False
2 False
3 True
4 False
5 True
6 False
dtype: bool
#这里drop也是一样的，可以用keep控制保留第一次出现的，还是最后1次出现的
data.drop_duplicates()
Out[104]:
K1 k2
0 one 1
2 one 2
3 two 3
5 two 4
这2个函数，默认都是判断全部的列，也可以指定列
data['v1']=range(7)
data
Out[118]:
K1 k2 v1
0 one 1 0
1 one 1 1
2 one 2 2
3 two 3 3
4 two 3 4
5 two 4 5
6 two 4 6
data.duplicated()
Out[119]:
0 False
1 False
2 False
3 False
4 False
5 False
6 False
dtype: bool
data.duplicated(subset=['K1'])
Out[120]:
0 False
1 True
2 True
3 False
4 True
5 True
6 True
dtype: bool
data.drop_duplicates(subset=['K1'])
Out[121]:
K1 k2 v1
0 one 1 0
3 two 3 3
2. 利用函数或映射进行数据转换
有的时候，我们想要对Series和dataframe中的每个元素做些转换，就用到了这个函数，和Python内置的map函数类似
Series.map(arg, na_action=None)
Map values of Series using input correspondence (which can be a dict, Series, or function)
arg : function, dict, or Series
DataFrame.applymap(func)
Apply a function to a DataFrame that is intended to operate elementwise, i.e. like doing map(func, series) for each series in the DataFrame
s1 = pd.Series(range(10))
s1
Out[133]:
0 0
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
dtype: int32
s1.map(lambda x:x+10)
Out[134]:
0 10
1 11
2 12
3 13
4 14
5 15
6 16
7 17
8 18
9 19
dtype: int64
s1.map(lambda x:x*-1)
Out[135]:
0 0
1 -1
2 -2
3 -3
4 -4
5 -5
6 -6
7 -7
8 -8
9 -9
dtype: int64
这个map，不单单可以是函数，还可以是 Series或dict
x = pd.Series([1,2,3], index=['one', 'two', 'three'])
y = pd.Series(['foo', 'bar', 'baz'], index=[1,2,3])
x
Out[142]:
one 1
two 2
three 3
dtype: int64
y
Out[143]:
1 foo
2 bar
3 baz
dtype: object
x.map(y)
Out[144]:
one foo
two bar
three baz
dtype: object
z = {1: 'A', 2: 'B', 3: 'C'}
x.map(z)
Out[146]:
one A
two B
three C
dtype: object
3.替换值
在字符串中，我们经常会用到replace函数，用来替换指定的值，pandas中也有
DataFrame.replace(to_replace=None, value=None, inplace=False, limit=None, regex=False, method='pad', axis=None)
Replace values given in ‘to_replace’ with ‘value’.
to_replace : str, regex, list, dict, Series, numeric, or None
Series.replace(to_replace=None, value=None, inplace=False, limit=None, regex=False, method='pad', axis=None)
Replace values given in ‘to_replace’ with ‘value’.
data = pd.Series([1., -999., 2., -999., -1000., 3.])
data
Out[150]:
0 1.0
1 -999.0
2 2.0
3 -999.0
4 -1000.0
5 3.0
dtype: float64
#我们将-999这个异常值替换掉
data.replace(-999,np.nan)
Out[151]:
0 1.0
1 NaN
2 2.0
3 NaN
4 -1000.0
5 3.0
dtype: float64
#同时替换多个值，也可以
data.replace([-999,-1000],np.nan)
Out[152]:
0 1.0
1 NaN
2 2.0
3 NaN
4 NaN
5 3.0
dtype: float64
data.replace([-999,-1000],[np.nan,0])
Out[153]:
0 1.0
1 NaN
2 2.0
3 NaN
4 0.0
5 3.0
dtype: float64
