Below I will share with you a basic operation method of the Python data analysis library pandas. It has a good reference value and I hope it will be helpful to everyone. Come and take a look together
#What is pandas?
Is that it?
. . . . Obviously pandas is not as cute as this guy. . . .
Let’s take a look at how pandas’ official website defines itself:
pandas is an open source, easy-to-use data structures and data analysis tools for the Python programming language.
Obviously, pandas is a very powerful data analysis library for Python!
Let’s learn it!
1.pandas sequence
import numpy as np import pandas as pd s_data = pd.Series([1,3,5,7,np.NaN,9,11])#pandas中生产序列的函数，类似于我们平时说的数组 print s_data
2.pandas data structure DataFrame
import numpy as np import pandas as pd #以20170220为基点向后生产时间点 dates = pd.date_range('20170220',periods=6) #DataFrame生成函数，行索引为时间点，列索引为ABCD data = pd.DataFrame(np.random.randn(6,4),index=dates,columns=list('ABCD')) print data print print data.shape print print data.values
3. Some operations of DataFrame (1)
import numpy as np
import pandas as pd
#设计一个字典
d_data = {'A':1,'B':pd.Timestamp('20170220'),'C':range(4),'D':np.arange(4)}
print d_data
#使用字典生成一个DataFrame
df_data = pd.DataFrame(d_data)
print df_data
#DataFrame中每一列的类型
print df_data.dtypes
#打印A列
print df_data.A
#打印B列
print df_data.B
#B列的类型
print type(df_data.B)
4. Some operations of DataFrame (2)
import numpy as np import pandas as pd dates = pd.date_range('20170220',periods=6) data = pd.DataFrame(np.random.randn(6,4),index=dates,columns=list('ABCD')) print data print #输出DataFrame头部数据，默认为前5行 print data.head() #输出输出DataFrame第一行数据 print data.head(1) #输出DataFrame尾部数据，默认为后5行 print data.tail() #输出输出DataFrame最后一行数据 print data.tail(1) #输出行索引 print data.index #输出列索引 print data.columns #输出DataFrame数据值 print data.values #输出DataFrame详细信息 print data.describe()
5. Some operations of DataFrame (3)
import numpy as np import pandas as pd dates = pd.date_range('20170220',periods=6) data = pd.DataFrame(np.random.randn(6,4),index=dates,columns=list('ABCD')) print data print #转置 print data.T #输出维度信息 print data.shape #转置后的维度信息 print data.T.shape #将列索引排序 print data.sort_index(axis = 1) #将列索引排序，降序排列 print data.sort_index(axis = 1,ascending=False) #将行索引排序，降序排列 print data.sort_index(axis = 0,ascending=False) #按照A列的值进行升序排列 print data.sort_values(by='A')
6 .Some operations on DataFrame (4)
##
import numpy as np import pandas as pd dates = pd.date_range('20170220',periods=6) data = pd.DataFrame(np.random.randn(6,4),index=dates,columns=list('ABCD')) print data #输出A列 print data.A #输出A列 print data['A'] #输出3,4行 print data[2:4] #输出3，4行 print data['20170222':'20170223'] #输出3,4行 print data.loc['20170222':'20170223'] #输出3,4行 print data.iloc[2:4] 输出B,C两列 print data.loc[:,['B','C']]
##7. Some operations of DataFrame (5)
import numpy as np import pandas as pd dates = pd.date_range('20170220',periods=6) data = pd.DataFrame(np.random.randn(6,4),index=dates,columns=list('ABCD')) print data #输出A列中大于0的行 print data[data.A > 0] #输出大于0的数据，小于等于0的用NaN补位 print data[data > 0] #拷贝data data2 = data.copy() print data2 tag = ['a'] * 2 + ['b'] * 2 + ['c'] * 2 #在data2中增加TAG列用tag赋值 data2['TAG'] = tag print data2 #打印TAG列中为a,c的行 print data2[data2.TAG.isin(['a','c'])]
import numpy as np import pandas as pd dates = pd.date_range('20170220',periods=6) data = pd.DataFrame(np.random.randn(6,4),index=dates,columns=list('ABCD')) print data #将第一行第一列元素赋值为100 data.iat[0,0] = 100 print data #将A列元素用range(6)赋值 data.A = range(6) print data #将B列元素赋值为200 data.B = 200 print data #将3,4列元素赋值为1000 data.iloc[:,2:5] = 1000 print data
##9.DataFrame Some operations (7)
import numpy as np
import pandas as pd
dates = pd.date_range('20170220',periods = 6)
df = pd.DataFrame(np.random.randn(6,4) , index = dates , columns = list('ABCD'))
print df
#重定义索引，并添加E列
dfl = df.reindex(index = dates[0:4],columns = list(df.columns)+['E'])
print dfl
#将E列中的2,3行赋值为2
dfl.loc[dates[1:3],'E'] = 2
print dfl
#去掉存在NaN元素的行
print dfl.dropna()
#将NaN元素赋值为5
print dfl.fillna(5)
#判断每个元素是否为NaN
print pd.isnull(dfl)
#求列平均值
print dfl.mean()
#对每列进行累加
print dfl.cumsum()
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10.Some of the DataFrame Operation (8)
import numpy as np
import pandas as pd
dates = pd.date_range('20170220',periods = 6)
df = pd.DataFrame(np.random.randn(6,4) , index = dates , columns = list('ABCD'))
print df
dfl = df.reindex(index = dates[0:4],columns = list(df.columns)+['E'])
print dfl
#针对行求平均值
print dfl.mean(axis=1)
#生成序列并向右平移两位
s = pd.Series([1,3,5,np.nan,6,8],index = dates).shift(2)
print s
#df与s做减法运算
print df.sub(s,axis = 'index')
#每列进行累加运算
print df.apply(np.cumsum)
#每列的最大值减去最小值
print df.apply(lambda x: x.max() - x.min())
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##11. Some operations of DataFrame (9)
import numpy as np
import pandas as pd
dates = pd.date_range('20170220',periods = 6)
df = pd.DataFrame(np.random.randn(6,4) , index = dates , columns = list('ABCD'))
print df
#定义一个函数
def _sum(x):
print(type(x))
return x.sum()
#apply函数可以接受一个函数作为参数
print df.apply(_sum)
s = pd.Series(np.random.randint(10,20,size = 15))
print s
#统计序列中每个元素出现的次数
print s.value_counts()
#返回出现次数最多的元素
print s.mode()
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12. Some operations of DataFrame ( 10)
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.randn(10,4) , columns = list('ABCD'))
print df
#合并函数
dfl = pd.concat([df.iloc[:3],df.iloc[3:7],df.iloc[7:]])
print dfl
#判断两个DataFrame中元素是否相等
print df == dfl
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13. Some operations of DataFrame (11 )
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.randn(10,4) , columns = list('ABCD'))
print df
left = pd.DataFrame({'key':['foo','foo'],'lval':[1,2]})
right = pd.DataFrame({'key':['foo','foo'],'rval':[4,5]})
print left
print right
#通过key来合并数据
print pd.merge(left,right,on='key')
s = pd.Series(np.random.randint(1,5,size = 4),index = list('ABCD'))
print s
#通过序列添加一行
print df.append(s,ignore_index = True)Copy after login
import numpy as np
import pandas as pd
df = pd.DataFrame({'A': ['foo','bar','foo','bar',
'foo','bar','foo','bar'],
'B': ['one','one','two','three',
'two','two','one','three'],
'C': np.random.randn(8),
'D': np.random.randn(8)})
print df
print
#根据A列的索引求和
print df.groupby('A').sum()
print
#先根据A列的索引，在根据B列的索引求和
print df.groupby(['A','B']).sum()
print
#先根据B列的索引，在根据A列的索引求和
print df.groupby(['B','A']).sum()Copy after login
##15. Some operations of DataFrame (13)
import pandas as pd
import numpy as np
#zip函数可以打包成一个个tuple
tuples = list(zip(*[['bar', 'bar', 'baz', 'baz',
'foo', 'foo', 'qux', 'qux'],
['one', 'two', 'one', 'two',
'one', 'two', 'one', 'two']]))
print tuples
#生成一个多层索引
index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
print index
print
df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=['A', 'B'])
print df
print
#将列索引变成行索引
print df.stack()
import pandas as pd
import numpy as np
tuples = list(zip(*[['bar', 'bar', 'baz', 'baz',
'foo', 'foo', 'qux', 'qux'],
['one', 'two', 'one', 'two',
'one', 'two', 'one', 'two']]))
index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=['A', 'B'])
print df
print
stacked = df.stack()
print stacked
#将行索引转换为列索引
print stacked.unstack()
#转换两次
print stacked.unstack().unstack()##17. Some operations of DataFrame (15)
import pandas as pd
import numpy as np
df = pd.DataFrame({'A' : ['one', 'one', 'two', 'three'] * 3,
'B' : ['A', 'B', 'C'] * 4,
'C' : ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 2,
'D' : np.random.randn(12),
'E' : np.random.randn(12)})
print df
#根据A，B索引为行，C的索引为列处理D的值
print pd.pivot_table(df, values='D', index=['A', 'B'], columns=['C'])
#感觉A列等于one为索引，根据C列组合的平均值
print df[df.A=='one'].groupby('C').mean()
18.时间序列(1)
import pandas as pd import numpy as np #创建一个以20170220为基准的以秒为单位的向前推进600个的时间序列 rng = pd.date_range('20170220', periods=600, freq='s') print rng #以时间序列为索引的序列 print pd.Series(np.random.randint(0, 500, len(rng)), index=rng)
19.时间序列(2)
import pandas as pd import numpy as np rng = pd.date_range('20170220', periods=600, freq='s') ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng) #重采样，以2分钟为单位进行加和采样 print ts.resample('2Min', how='sum') #列出2011年1季度到2017年1季度 rng1 = pd.period_range('2011Q1','2017Q1',freq='Q') print rng1 #转换成时间戳形式 print rng1.to_timestamp() #时间加减法 print pd.Timestamp('20170220') - pd.Timestamp('20170112') print pd.Timestamp('20170220') + pd.Timedelta(days=12)
20.数据类别
import pandas as pd
import numpy as np
df = pd.DataFrame({"id":[1,2,3,4,5,6], "raw_grade":['a', 'b', 'b', 'a', 'a', 'e']})
print df
#添加类别数据，以raw_grade的值为类别基础
df["grade"] = df["raw_grade"].astype("category")
print df
#打印类别
print df["grade"].cat.categories
#更改类别
df["grade"].cat.categories = ["very good", "good", "very bad"]
print df
#根据grade的值排序
print df.sort_values(by='grade', ascending=True)
#根据grade排序显示数量
print df.groupby("grade").size()
21.数据可视化
import pandas as pd import numpy as np import matplotlib.pyplot as plt ts = pd.Series(np.random.randn(1000), index=pd.date_range('20170220', periods=1000)) ts = ts.cumsum() print ts ts.plot() plt.show()
22.数据读写
import pandas as pd import numpy as np df = pd.DataFrame(np.random.randn(10, 4), columns=list('ABCD')) #数据保存，相对路径 df.to_csv('data.csv') #数据读取 print pd.read_csv('data.csv', index_col=0)
数据被保存到这个文件中：
打开看看：
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