Panda 安装
MacOS:
# 使用 python 3+:
pip3 install pandas
# 使用 python 2+:
pip install pandas
Ubuntu:
sudo apt-get install python-pandas
Pandas的基本介绍
Pandas与Numpy的不同与联系
如果用Python中的list和dict做类比的化,那么Numpy是list,Pandas就是dict。
Pandas有两个基本的数据结构:Series和DataFrame。
series
Series的字符串表现形式为:索引在左边,值在右边。由于我们没有为数据指定索引。于是会自动创建一个0到N-1(N为长度)的整数型索引:
import pandas as pd
import numpy as np
s = pd.Series([1,3,6,np.nan,44,1])
print(s)
"""
0 1.0
1 3.0
2 6.0
3 NaN
4 44.0
5 1.0
dtype: float64
"""
DataFrame
DataFrame是一个表格型的数据结构,它包含有一组有序的列,每列可以使不同的数据类型(数字,字符串,bool)。DataFrame既有行索引也有列索引, 它可以被看做由Series组成的大字典。没有给定索引的话会使用从0开始的默认索引。
dates = pd.date_range('20160101',periods=6)
df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=['a','b','c','d'])
print(df)
"""
a b c d
2016-01-01 -0.253065 -2.071051 -0.640515 0.613663
2016-01-02 -1.147178 1.532470 0.989255 -0.499761
2016-01-03 1.221656 -2.390171 1.862914 0.778070
2016-01-04 1.473877 -0.046419 0.610046 0.204672
2016-01-05 -1.584752 -0.700592 1.487264 -1.778293
2016-01-06 0.633675 -1.414157 -0.277066 -0.442545
"""
另一种生成DataFrame的方法:
df2 = pd.DataFrame({'A' : 1.,
'B' : pd.Timestamp('20130102'),
'C' : pd.Series(1,index=list(range(4)),dtype='float32'),
'D' : np.array([3] * 4,dtype='int32'),
'E' : pd.Categorical(["test","train","test","train"]),
'F' : 'foo'})
print(df2)
"""
A B C D E F
0 1.0 2013-01-02 1.0 3 test foo
1 1.0 2013-01-02 1.0 3 train foo
2 1.0 2013-01-02 1.0 3 test foo
3 1.0 2013-01-02 1.0 3 train foo
"""
挑选b列元素:
print(df['b'])
"""
2016-01-01 -2.071051
2016-01-02 1.532470
2016-01-03 -2.390171
2016-01-04 -0.046419
2016-01-05 -0.700592
2016-01-06 -1.414157
Freq: D, Name: b, dtype: float64
"""
由于DataFrame中的每一个类都是相互独立的,所以有自己的属性特征:
print(df2.dtypes)
"""
df2.dtypes
A float64
B datetime64[ns]
C float32
D int32
E category
F object
dtype: object
"""
查看索引:
print(df2.index)
# Int64Index([0, 1, 2, 3], dtype='int64')
查看列名称:
print(df2.columns)
# Index(['A', 'B', 'C', 'D', 'E', 'F'], dtype='object')
只查看值(不看索引信息):
print(df2.values)
"""
array([[1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'test', 'foo'],
[1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'train', 'foo'],
[1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'test', 'foo'],
[1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'train', 'foo']], dtype=object)
"""
查看数据总结:
df2.describe()
"""
A C D
count 4.0 4.0 4.0
mean 1.0 1.0 3.0
std 0.0 0.0 0.0
min 1.0 1.0 3.0
25% 1.0 1.0 3.0
50% 1.0 1.0 3.0
75% 1.0 1.0 3.0
max 1.0 1.0 3.0
"""
执行数据的转置:
print(df2.T)
"""
0 1 2 \
A 1 1 1
B 2013-01-02 00:00:00 2013-01-02 00:00:00 2013-01-02 00:00:00
C 1 1 1
D 3 3 3
E test train test
F foo foo foo
3
A 1
B 2013-01-02 00:00:00
C 1
D 3
E train
F foo
"""
排序:
#对数据的index进行反向排序
print(df2.sort_index(axis=1, ascending=False))
"""
F E D C B A
0 foo test 3 1.0 2013-01-02 1.0
1 foo train 3 1.0 2013-01-02 1.0
2 foo test 3 1.0 2013-01-02 1.0
3 foo train 3 1.0 2013-01-02 1.0
"""
#对数据的value进行排序(对于B列)
print(df2.sort_values(by='B'))
"""
A B C D E F
0 1.0 2013-01-02 1.0 3 test foo
1 1.0 2013-01-02 1.0 3 train foo
2 1.0 2013-01-02 1.0 3 test foo
3 1.0 2013-01-02 1.0 3 train foo
"""
Pandas选择数据
首先建立一个6×4的矩阵:
dates = pd.date_range('23130101',periods=6)
df = pd.DataFrame(np.arange(24).reshape((6,4)),index=dates,column=['A','B','C','D'])
"""
A B C D
2013-01-01 0 1 2 3
2013-01-02 4 5 6 7
2013-01-03 8 9 10 11
2013-01-04 12 13 14 15
2013-01-05 16 17 18 19
2013-01-06 20 21 22 23
"""
简单的筛选
按照属性筛选某一列的数据
print(df['A'])
print(df.A)
"""
2013-01-01 0
2013-01-02 4
2013-01-03 8
2013-01-04 12
2013-01-05 16
2013-01-06 20
Freq: D, Name: A, dtype: int64
"""
按照行号筛选多行数据:(这里只有0,1,2行)
print(df[0:3])
"""
A B C D
2013-01-01 0 1 2 3
2013-01-02 4 5 6 7
2013-01-03 8 9 10 11
"""
按照index筛选多行的数据:(包括前后两个标签)
print(df['20130102':'20130104'])
"""
A B C D
2013-01-02 4 5 6 7
2013-01-03 8 9 10 11
2013-01-04 12 13 14 15
"""
选择数据也可以使用标签 Loc:
print(df.loc['20130102'])
"""
A 4
B 5
C 6
D 7
Name: 2013-01-02 00:00:00, dtype: int64
"""
print(df.loc[:,['A','B']])
"""
A B
2013-01-01 0 1
2013-01-02 4 5
2013-01-03 8 9
2013-01-04 12 13
2013-01-05 16 17
2013-01-06 20 21
"""
print(df.loc['20130102',['A','B']])
"""
A 4
B 5
Name: 2013-01-02 00:00:00, dtype: int64
"""
或者直接使用序列 iloc:
print(df.iloc[3,1])
# 13
print(df.iloc[3:5,1:3])
"""
B C
2013-01-04 13 14
2013-01-05 17 18
"""
print(df.iloc[[1,3,5],1:3])
"""
B C
2013-01-02 5 6
2013-01-04 13 14
2013-01-06 21 22
"""
以上两种的混合使用:
print(df.ix[:3,['A','C']])
"""
A C
2013-01-01 0 2
2013-01-02 4 6
2013-01-03 8 10
"""
通过布尔判断进行筛选:booling indexing
print(df[df.A>8])
"""
A B C D
2013-01-04 12 13 14 15
2013-01-05 16 17 18 19
2013-01-06 20 21 22 23
"""
给Pandas中的数据设置值
创建示例矩阵:
dates = pd.date_range('20130101', periods=6)
df = pd.DataFrame(np.arange(24).reshape((6,4)),index=dates, columns=['A','B','C','D'])
"""
A B C D
2013-01-01 0 1 2 3
2013-01-02 4 5 6 7
2013-01-03 8 9 10 11
2013-01-04 12 13 14 15
2013-01-05 16 17 18 19
2013-01-06 20 21 22 23
"""
根据位置设置loc和iloc:
df.iloc[2,2] = 1111
df.loc['20130101','B'] = 2222
"""
A B C D
2013-01-01 0 2222 2 3
2013-01-02 4 5 6 7
2013-01-03 8 9 1111 11
2013-01-04 12 13 14 15
2013-01-05 16 17 18 19
2013-01-06 20 21 22 23
"""
按行或者按列设置:
df['F'] = np.nan
"""
A B C D F
2013-01-01 0 2222 2 3 NaN
2013-01-02 4 5 6 7 NaN
2013-01-03 8 0 1111 11 NaN
2013-01-04 12 0 14 15 NaN
2013-01-05 16 0 18 19 NaN
2013-01-06 20 0 22 23 NaN
"""
根据条件设置:一般都是判断条件
df.B[df.A>4] = 0
"""
A B C D
2013-01-01 0 2222 2 3
2013-01-02 4 5 6 7
2013-01-03 8 0 1111 11
2013-01-04 12 0 14 15
2013-01-05 16 0 18 19
2013-01-06 20 0 22 23
"""
添加数据:
#加上一个Series序列,但是序列的长度需要对齐
df.B[df.A>4] = 0
"""
A B C D
2013-01-01 0 2222 2 3
2013-01-02 4 5 6 7
2013-01-03 8 0 1111 11
2013-01-04 12 0 14 15
2013-01-05 16 0 18 19
2013-01-06 20 0 22 23
"""
Pandas处理丢失数据
针对有时候出现的导入、处理数据时数据会有缺失的情况,即会产生空的或者是NaN的数据,这里讲如何删除或者填补数据。
建立示例矩阵(6*4,并将两个位置设置为NaN)
dates = pd.date_range('20130101', periods=6)
df = pd.DataFrame(np.arange(24).reshape((6,4)),index=dates, columns=['A','B','C','D'])
df.iloc[0,1] = np.nan
df.iloc[1,2] = np.nan
"""
A B C D
2013-01-01 0 NaN 2.0 3
2013-01-02 4 5.0 NaN 7
2013-01-03 8 9.0 10.0 11
2013-01-04 12 13.0 14.0 15
2013-01-05 16 17.0 18.0 19
2013-01-06 20 21.0 22.0 23
"""
去除含有NaN的行或者列,使用dropna():
df.dropna(
axis=0, # 0: 对行进行操作; 1: 对列进行操作
how='any' # 'any': 只要存在 NaN 就 drop 掉; 'all': 必须全部是 NaN 才 drop
)
"""
A B C D
2013-01-03 8 9.0 10.0 11
2013-01-04 12 13.0 14.0 15
2013-01-05 16 17.0 18.0 19
2013-01-06 20 21.0 22.0 23
"""
将值是NaN的部分赋值,使用pd.fillna():
df.fillna(value=0)
"""
A B C D
2013-01-01 0 0.0 2.0 3
2013-01-02 4 5.0 0.0 7
2013-01-03 8 9.0 10.0 11
2013-01-04 12 13.0 14.0 15
2013-01-05 16 17.0 18.0 19
2013-01-06 20 21.0 22.0 23
"""
但是什么时候我们知道我们需要使用上面的语句来对NaN进行操作呢?
判断是否有缺失数据:True表示确实数据
df.isnull()
"""
A B C D
2013-01-01 False True False False
2013-01-02 False False True False
2013-01-03 False False False False
2013-01-04 False False False False
2013-01-05 False False False False
2013-01-06 False False False False
"""
检测数据中是否存在NaN,存在返回True:
print(np.any(df.isnull())) == True
# True
Pandas导入导出
Pandas可以读取、存取的资料格式有很多
读取csv
import pandas as pd
data = pd.read_csv('hello.csv')
print(data)
将资料转存成pickle文件:
data.to_pickle('hello.pickle')
Pandas 合并之concat
pandas处理数据往往需要将数据进行合并处理,concat就是一种基本的合并方式。
axis(合并方向)
axis=0为预设值,未设定任何参数时,将以vertical的方向进行合并:
import pandas as pd
import numpy as np
#定义资料集
df1 = pd.DataFrame(np.ones((3,4))*0, columns=['a','b','c','d'])
df2 = pd.DataFrame(np.ones((3,4))*1, columns=['a','b','c','d'])
df3 = pd.DataFrame(np.ones((3,4))*2, columns=['a','b','c','d'])
#concat纵向合并
res = pd.concat([df1, df2, df3], axis=0)
#打印结果
print(res)
# a b c d
# 0 0.0 0.0 0.0 0.0
# 1 0.0 0.0 0.0 0.0
# 2 0.0 0.0 0.0 0.0
# 0 1.0 1.0 1.0 1.0
# 1 1.0 1.0 1.0 1.0
# 2 1.0 1.0 1.0 1.0
# 0 2.0 2.0 2.0 2.0
# 1 2.0 2.0 2.0 2.0
# 2 2.0 2.0 2.0 2.0
可以看到上面的输出结果的index值还是之前的DataFrame中的值,为了使新产生的DataFrame有适应自身的index,在进行concat操作的时候需要使用ignore——index参数来重置index:
#承上一个例子,并将index_ignore设定为True
res = pd.concat([df1, df2, df3], axis=0, ignore_index=True)
#打印结果
print(res)
# a b c d
# 0 0.0 0.0 0.0 0.0
# 1 0.0 0.0 0.0 0.0
# 2 0.0 0.0 0.0 0.0
# 3 1.0 1.0 1.0 1.0
# 4 1.0 1.0 1.0 1.0
# 5 1.0 1.0 1.0 1.0
# 6 2.0 2.0 2.0 2.0
# 7 2.0 2.0 2.0 2.0
# 8 2.0 2.0 2.0 2.0
join参数指定合并的方式:
join='outer'是预设值,未加任何参数的时候,函数默认按照column来进行纵向的合并,将相同column上下合并在一起,其他的column各自成列,原本没有的位置使用NaN来进行填充。
import pandas as pd
import numpy as np
#定义资料集
df1 = pd.DataFrame(np.ones((3,4))*0, columns=['a','b','c','d'], index=[1,2,3])
df2 = pd.DataFrame(np.ones((3,4))*1, columns=['b','c','d','e'], index=[2,3,4])
#纵向"外"合并df1与df2
res = pd.concat([df1, df2], axis=0, join='outer')
print(res)
# a b c d e
# 1 0.0 0.0 0.0 0.0 NaN
# 2 0.0 0.0 0.0 0.0 NaN
# 3 0.0 0.0 0.0 0.0 NaN
# 2 NaN 1.0 1.0 1.0 1.0
# 3 NaN 1.0 1.0 1.0 1.0
# 4 NaN 1.0 1.0 1.0 1.0
与之对应的是join='inner'下的条件,只有相同的column会被保留
#承上一个例子
#纵向"内"合并df1与df2
res = pd.concat([df1, df2], axis=0, join='inner')
#打印结果
print(res)
# b c d
# 1 0.0 0.0 0.0
# 2 0.0 0.0 0.0
# 3 0.0 0.0 0.0
# 2 1.0 1.0 1.0
# 3 1.0 1.0 1.0
# 4 1.0 1.0 1.0
#重置index并打印结果
res = pd.concat([df1, df2], axis=0, join='inner', ignore_index=True)
print(res)
# b c d
# 0 0.0 0.0 0.0
# 1 0.0 0.0 0.0
# 2 0.0 0.0 0.0
# 3 1.0 1.0 1.0
# 4 1.0 1.0 1.0
# 5 1.0 1.0 1.0
join_axes(按照axes合并)
import pandas as pd
import numpy as np
#定义资料集
df1 = pd.DataFrame(np.ones((3,4))*0, columns=['a','b','c','d'], index=[1,2,3])
df2 = pd.DataFrame(np.ones((3,4))*1, columns=['b','c','d','e'], index=[2,3,4])
#依照`df1.index`进行横向合并
res = pd.concat([df1, df2], axis=1, join_axes=[df1.index])
#打印结果
print(res)
# a b c d b c d e
# 1 0.0 0.0 0.0 0.0 NaN NaN NaN NaN
# 2 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0
# 3 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0
#移除join_axes,并打印结果
res = pd.concat([df1, df2], axis=1)
print(res)
# a b c d b c d e
# 1 0.0 0.0 0.0 0.0 NaN NaN NaN NaN
# 2 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0
# 3 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0
# 4 NaN NaN NaN NaN 1.0 1.0 1.0 1.0
append(添加数据):append只有纵向的合并没有横向的合并
import pandas as pd
import numpy as np
#定义资料集
df1 = pd.DataFrame(np.ones((3,4))*0, columns=['a','b','c','d'])
df2 = pd.DataFrame(np.ones((3,4))*1, columns=['a','b','c','d'])
df3 = pd.DataFrame(np.ones((3,4))*1, columns=['a','b','c','d'])
s1 = pd.Series([1,2,3,4], index=['a','b','c','d'])
#将df2合并到df1的下面,以及重置index,并打印出结果
res = df1.append(df2, ignore_index=True)
print(res)
# a b c d
# 0 0.0 0.0 0.0 0.0
# 1 0.0 0.0 0.0 0.0
# 2 0.0 0.0 0.0 0.0
# 3 1.0 1.0 1.0 1.0
# 4 1.0 1.0 1.0 1.0
# 5 1.0 1.0 1.0 1.0
#合并多个df,将df2与df3合并至df1的下面,以及重置index,并打印出结果
res = df1.append([df2, df3], ignore_index=True)
print(res)
# a b c d
# 0 0.0 0.0 0.0 0.0
# 1 0.0 0.0 0.0 0.0
# 2 0.0 0.0 0.0 0.0
# 3 1.0 1.0 1.0 1.0
# 4 1.0 1.0 1.0 1.0
# 5 1.0 1.0 1.0 1.0
# 6 1.0 1.0 1.0 1.0
# 7 1.0 1.0 1.0 1.0
# 8 1.0 1.0 1.0 1.0
#合并series,将s1合并至df1,以及重置index,并打印出结果
res = df1.append(s1, ignore_index=True)
print(res)
# a b c d
# 0 0.0 0.0 0.0 0.0
# 1 0.0 0.0 0.0 0.0
# 2 0.0 0.0 0.0 0.0
# 3 1.0 2.0 3.0 4.0
Pandas合并之merge
pandas中merge和concat类似,但是主要用于两组有key,column的数据,统一索引的数据,DataBase。
根据一组key合并
import pandas as pd
#定义资料集并打印出
left = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],
'A': ['A0', 'A1', 'A2', 'A3'],
'B': ['B0', 'B1', 'B2', 'B3']})
right = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],
'C': ['C0', 'C1', 'C2', 'C3'],
'D': ['D0', 'D1', 'D2', 'D3']})
print(left)
# A B key
# 0 A0 B0 K0
# 1 A1 B1 K1
# 2 A2 B2 K2
# 3 A3 B3 K3
print(right)
# C D key
# 0 C0 D0 K0
# 1 C1 D1 K1
# 2 C2 D2 K2
# 3 C3 D3 K3
#依据key column合并,并打印出
res = pd.merge(left, right, on='key')
print(res)
A B key C D
# 0 A0 B0 K0 C0 D0
# 1 A1 B1 K1 C1 D1
# 2 A2 B2 K2 C2 D2
# 3 A3 B3 K3 C3 D3
根据两组key合并:
合并时有4种方法how = ['left', 'right', 'outer', 'inner'],预设值how='inner'。
import pandas as pd
#定义资料集并打印出
left = pd.DataFrame({'key1': ['K0', 'K0', 'K1', 'K2'],
'key2': ['K0', 'K1', 'K0', 'K1'],
'A': ['A0', 'A1', 'A2', 'A3'],
'B': ['B0', 'B1', 'B2', 'B3']})
right = pd.DataFrame({'key1': ['K0', 'K1', 'K1', 'K2'],
'key2': ['K0', 'K0', 'K0', 'K0'],
'C': ['C0', 'C1', 'C2', 'C3'],
'D': ['D0', 'D1', 'D2', 'D3']})
print(left)
# A B key1 key2
# 0 A0 B0 K0 K0
# 1 A1 B1 K0 K1
# 2 A2 B2 K1 K0
# 3 A3 B3 K2 K1
print(right)
# C D key1 key2
# 0 C0 D0 K0 K0
# 1 C1 D1 K1 K0
# 2 C2 D2 K1 K0
# 3 C3 D3 K2 K0
#依据key1与key2 columns进行合并,并打印出四种结果['left', 'right', 'outer', 'inner']
res = pd.merge(left, right, on=['key1', 'key2'], how='inner')
print(res)
# A B key1 key2 C D
# 0 A0 B0 K0 K0 C0 D0
# 1 A2 B2 K1 K0 C1 D1
# 2 A2 B2 K1 K0 C2 D2
res = pd.merge(left, right, on=['key1', 'key2'], how='outer')
print(res)
# A B key1 key2 C D
# 0 A0 B0 K0 K0 C0 D0
# 1 A1 B1 K0 K1 NaN NaN
# 2 A2 B2 K1 K0 C1 D1
# 3 A2 B2 K1 K0 C2 D2
# 4 A3 B3 K2 K1 NaN NaN
# 5 NaN NaN K2 K0 C3 D3
res = pd.merge(left, right, on=['key1', 'key2'], how='left')
print(res)
# A B key1 key2 C D
# 0 A0 B0 K0 K0 C0 D0
# 1 A1 B1 K0 K1 NaN NaN
# 2 A2 B2 K1 K0 C1 D1
# 3 A2 B2 K1 K0 C2 D2
# 4 A3 B3 K2 K1 NaN NaN
res = pd.merge(left, right, on=['key1', 'key2'], how='right')
print(res)
# A B key1 key2 C D
# 0 A0 B0 K0 K0 C0 D0
# 1 A2 B2 K1 K0 C1 D1
# 2 A2 B2 K1 K0 C2 D2
# 3 NaN NaN K2 K0 C3 D3
indicator参数:显示合并的记录(需要将indicator的值置为True)
import pandas as pd
#定义资料集并打印出
df1 = pd.DataFrame({'col1':[0,1], 'col_left':['a','b']})
df2 = pd.DataFrame({'col1':[1,2,2],'col_right':[2,2,2]})
print(df1)
# col1 col_left
# 0 0 a
# 1 1 b
print(df2)
# col1 col_right
# 0 1 2
# 1 2 2
# 2 2 2
# 依据col1进行合并,并启用indicator=True,最后打印出
res = pd.merge(df1, df2, on='col1', how='outer', indicator=True)
print(res)
# col1 col_left col_right _merge
# 0 0.0 a NaN left_only
# 1 1.0 b 2.0 both
# 2 2.0 NaN 2.0 right_only
# 3 2.0 NaN 2.0 right_only
# 自定indicator column的名称,并打印出
res = pd.merge(df1, df2, on='col1', how='outer', indicator='indicator_column')
print(res)
# col1 col_left col_right indicator_column
# 0 0.0 a NaN left_only
# 1 1.0 b 2.0 both
# 2 2.0 NaN 2.0 right_only
# 3 2.0 NaN 2.0 right_only
依据index进行合并:可以将index也理解为一种key
import pandas as pd
#定义资料集并打印出
left = pd.DataFrame({'A': ['A0', 'A1', 'A2'],
'B': ['B0', 'B1', 'B2']},
index=['K0', 'K1', 'K2'])
right = pd.DataFrame({'C': ['C0', 'C2', 'C3'],
'D': ['D0', 'D2', 'D3']},
index=['K0', 'K2', 'K3'])
print(left)
# A B
# K0 A0 B0
# K1 A1 B1
# K2 A2 B2
print(right)
# C D
# K0 C0 D0
# K2 C2 D2
# K3 C3 D3
#依据左右资料集的index进行合并,how='outer',并打印出
res = pd.merge(left, right, left_index=True, right_index=True, how='outer')
print(res)
# A B C D
# K0 A0 B0 C0 D0
# K1 A1 B1 NaN NaN
# K2 A2 B2 C2 D2
# K3 NaN NaN C3 D3
#依据左右资料集的index进行合并,how='inner',并打印出
res = pd.merge(left, right, left_index=True, right_index=True, how='inner')
print(res)
# A B C D
# K0 A0 B0 C0 D0
# K2 A2 B2 C2 D2
解决overlapping:使用suffixes参数
import pandas as pd
#定义资料集
boys = pd.DataFrame({'k': ['K0', 'K1', 'K2'], 'age': [1, 2, 3]})
girls = pd.DataFrame({'k': ['K0', 'K0', 'K3'], 'age': [4, 5, 6]})
#使用suffixes解决overlapping的问题
res = pd.merge(boys, girls, on='k', suffixes=['_boy', '_girl'], how='inner')
print(res)
# age_boy k age_girl
# 0 1 K0 4
# 1 1 K0 5
Pandas plot出图(数据可视化)
需要使用的Module:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
创建一个Series:Series的可视化
# 随机生成1000个数据
data = pd.Series(np.random.randn(1000),index=np.arange(1000))
# 为了方便观看效果, 我们累加这个数据
data.cumsum()
# pandas 数据可以直接观看其可视化形式
data.plot() #使用plt.plot(x= , y=) 但是由于data本身就是一个数据所以是不需要传入x=,y= 的
plt.show()
DataFram的可视化
data = pd.DataFrame(
np.random.randn(1000,4),
index=np.arange(1000),
columns=list("ABCD")
)
data.cumsum()
data.plot()
plt.show()
如果我们希望显示散点图:使用scatter
ax = data.plot.scatter(x='A',y='B',color='DarkBlue',label='Class1')
# 将之下这个 data 画在上一个 ax 上面
data.plot.scatter(x='A',y='C',color='LightGreen',label='Class2',ax=ax)
plt.show()