pandas知识点（基本功能）
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2022-07-05 14:41:17
1.重新索引 如果reindex会根据新索引重新排序，不存在的则引入缺省： In [3]: obj = Series([4.5,7.2,-5.3,3.6], index=["d","b","a","c"]) In [4]: obj Out[4]: d 4.5 b 7.2 a -5.3 c 3.6 d ......
1.重新索引
如果reindex会根据新索引重新排序，不存在的则引入缺省：
in [3]: obj = series([4.5,7.2,-5.3,3.6], index=["d","b","a","c"]) in [4]: obj out[4]: d 4.5 b 7.2 a -5.3 c 3.6 dtype: float64 in [6]: obj2 = obj.reindex(["a","b","c","d","e"]) in [7]: obj2 out[7]: a -5.3 b 7.2 c 3.6 d 4.5 e nan dtype: float64
ffill可以实现前向值填充：
in [8]: obj3 = series(["blue","purple","yellow"], index=[0,2,4]) in [9]: obj3.reindex(range(6), method="ffill") out[9]: 0 blue 1 blue 2 purple 3 purple 4 yellow 5 yellow dtype: object
2.丢弃指定轴上的项
drop方法返回在指定轴上删除了指定值的新对象：
in [12]: obj = series(np.arange(5.), index=["a","b","c","d","e"])
in [13]: new_obj = obj.drop("c")
in [14]: new_obj
out[14]:
a 0.0
b 1.0
d 3.0
e 4.0
dtype: float64
dataframe可以删除任意轴上的索引值
3.索引，选取和过滤
series的索引可以不止是整数：
in [4]: obj = series(np.arange(4.), index=["a","b","c","d"])out[6]: a 0.0 b 1.0 dtype: float64 in [7]: obj[obj<2] out[7]: a 0.0 b 1.0 dtype: float64
series切片与普通的python切片不一样，末端也是包含的：
in [8]: obj["b":"c"] out[8]: b 1.0 c 2.0 dtype: float64
dataframe进行索引：
in [10]: data
out[10]:
one two three four
ohio 0 1 2 3
colorado 4 5 6 7
utah 8 9 10 11
new york 12 13 14 15
in [11]: data['two']
out[11]:
ohio 1
colorado 5
utah 9
new york 13
name: two, dtype: int32
in [12]: data[:2]
out[12]:
one two three four
ohio 0 1 2 3
colorado 4 5 6 7
布尔型dataframe进行索引：
in [13]: data > 5
out[13]:
one two three four
ohio false false false false
colorado false false true true
utah true true true true
new york true true true true
利用ix可以选取行和列的子集：
in [18]: data.ix['colorado',['two','three']]
out[18]:
two 5
three 6
name: colorado, dtype: int32
in [19]: data.ix[['colorado','utah'],[3,0,1]]
out[19]:
four one two
colorado 7 4 5
utah 11 8 9
4.算数运算和数据对齐
对不同索引的对象进行算数运算，如果存在不同的索引，则结果的索引取其并集：
in [20]: s1 = series([7.3,-2.5,3.4,1.5],index=['a','c','d','e']) in [21]: s2 = series([-2.1, 3.6, -1.5, 4, 3.1],index=['a','c','e','f','g']) in [22]: s1+s2 out[22]: a 5.2 c 1.1 d nan e 0.0 f nan g nan dtype: float64
对于dataframe，对齐操作会同时发生在行和列上：
in [26]: df1
out[26]:
b d e
utah 0.0 1.0 2.0
ohio 3.0 4.0 5.0
texas 6.0 7.0 8.0
oregon 9.0 10.0 11.0
in [27]: df2
out[27]:
b c d
ohio 0.0 1.0 2.0
texas 3.0 4.0 5.0
colorado 6.0 7.0 8.0
in [28]: df1+df2
out[28]:
b c d e
colorado nan nan nan nan
ohio 3.0 nan 6.0 nan
oregon nan nan nan nan
texas 9.0 nan 12.0 nan
utah nan nan nan nan
使用add方法相加：
in [30]: df2.add(df1,fill_value=0)
out[30]:
b c d e
colorado 6.0 7.0 8.0 nan
ohio 3.0 1.0 6.0 5.0
oregon 9.0 nan 10.0 11.0
texas 9.0 4.0 12.0 8.0
utah 0.0 nan 1.0 2.0
5.dataframe和series之间的运算：
计算二维数组和某一行的差：
in [31]: arr = np.arange(12.).reshape((3,4))
in [32]: arr
out[32]:
array([[ 0., 1., 2., 3.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.]])
in [33]: arr - arr[1]
out[33]:
array([[-4., -4., -4., -4.],
[ 0., 0., 0., 0.],
[ 4., 4., 4., 4.]])
dataframe和series之间的运算：
in [35]: frame = dataframe(np.arange(12.).reshape((4,3)),columns=list('bde'),index=['utah','ohio','texas','oregon'])
in [39]: series = frame.iloc[0]
in [40]: frame
out[40]:
b d e
utah 0.0 1.0 2.0
ohio 3.0 4.0 5.0
texas 6.0 7.0 8.0
oregon 9.0 10.0 11.0
in [41]: series
out[41]:
b 0.0
d 1.0
e 2.0
name: utah, dtype: float64
in [43]: frame - series
out[43]:
b d e
utah 0.0 0.0 0.0
ohio 3.0 3.0 3.0
texas 6.0 6.0 6.0
oregon 9.0 9.0 9.0
如果某个索引值找不到，则与运算的两个对象会被重新索引以形成并集：
in [45]: frame + series2
out[45]:
b d e f
utah 0.0 nan 3.0 nan
ohio 3.0 nan 6.0 nan
texas 6.0 nan 9.0 nan
oregon 9.0 nan 12.0 nan
匹配列并在列上广播：
in [46]: series3 = frame['d']
in [47]: frame.sub(series3, axis=0)
out[47]:
b d e
utah -1.0 0.0 1.0
ohio -1.0 0.0 1.0
texas -1.0 0.0 1.0
oregon -1.0 0.0 1.0
6.函数应用和映射
numpy的ufuncs也可用于操作pandas对象：
in [49]: frame = dataframe(np.random.randn(4,3), columns=list('bde'),index=['utah','ohio','texas','oregon'])
in [50]: frame
out[50]:
b d e
utah 0.913051 -1.289725 -0.590573
ohio 1.417612 -1.835357 -0.010755
texas 0.328839 -0.121878 -1.209583
oregon 1.315330 -1.026557 -1.777427
in [51]: np.abs(frame)
out[51]:
b d e
utah 0.913051 1.289725 0.590573
ohio 1.417612 1.835357 0.010755
texas 0.328839 0.121878 1.209583
oregon 1.315330 1.026557 1.777427
dataframe的apply方法可以实现将函数应用到由各行或列形成的一维数组上：
in [52]: f = lambda x:x.max() - x.min()
in [53]: frame.apply(f)
out[53]:
b 1.088773
d 1.713479
e 1.766671
dtype: float64
in [54]: frame.apply(f, axis=1)
out[54]:
utah 2.202776
ohio 3.252969
texas 1.538421
oregon 3.092757
dtype: float64
7.排序和排名
sort_index方法可以返回一个已排序的对象
in [57]: obj = series(range(4), index=['d','a','b','c'])
in [58]: obj
out[58]:
d 0
a 1
b 2
c 3
dtype: int64
in [59]: obj.sort_index
out[59]:
<bound method series.sort_index of d 0
a 1
b 2
c 3
dtype: int64>
in [62]: frame.sort_index()
out[62]:
b d e
ohio 1.417612 -1.835357 -0.010755
oregon 1.315330 -1.026557 -1.777427
texas 0.328839 -0.121878 -1.209583
utah 0.913051 -1.289725 -0.590573
in [63]: frame.sort_index(axis=1)
out[63]:
b d e
utah 0.913051 -1.289725 -0.590573
ohio 1.417612 -1.835357 -0.010755
texas 0.328839 -0.121878 -1.209583
oregon 1.315330 -1.026557 -1.777427
倒序查看：
in [65]: frame.sort_index(axis=1,ascending=false)
out[65]:
e d b
utah -0.590573 -1.289725 0.913051
ohio -0.010755 -1.835357 1.417612
texas -1.209583 -0.121878 0.328839
oregon -1.777427 -1.026557 1.315330
按某一列的值进行排序：
in [67]: frame.sort_values(by='b')
out[67]:
b d e
texas 0.328839 -0.121878 -1.209583
utah 0.913051 -1.289725 -0.590573
oregon 1.315330 -1.026557 -1.777427
ohio 1.417612 -1.835357 -0.010755
排名（rank）与排序类似，它会设置一个排名值，并且可以根据某种规则破坏平级关系
in [70]: obj out[70]: 0 7 1 -5 2 7 3 4 4 2 5 0 6 4 dtype: int64 in [71]: obj.rank() out[71]: 0 6.5 1 1.0 2 6.5 3 4.5 4 3.0 5 2.0 6 4.5 dtype: float64
根据值在原数据中出现的顺序给出排名
in [72]: obj.rank(method='first') out[72]: 0 6.0 1 1.0 2 7.0 3 4.0 4 3.0 5 2.0 6 5.0 dtype: float64
8.带有重复值的轴索引
使用is_unique查看值是否唯一
in [73]: obj = series(range(5),index=['a','a','b','b','c']) in [74]: obj out[74]: a 0 a 1 b 2 b 3 c 4 dtype: int64 in [75]: obj.index.is_unique out[75]: false
对重复索引选取数据：
in [76]: obj['a'] out[76]: a 0 a 1 dtype: int64
dataframe也是同样的道理