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Pandas学习笔记02

31 Dec 2017 |

Pandas学习笔记02

1. pandas.DataFrame结构

pandas.DataFrame(data,index,columns,dtype,copy)

2. 创建DataFrame

Pandas的DataFrame可以使用以下输入创建：

  • 列表
  • 字典
  • Series
  • Numpy模块的ndarray
  • 其他DataFrame

3. 创建空DataFrame

import pandas as pd
df = pd.DataFrame()
print df

Empty DataFrame
Columns: []
Index: []

4. 从列表创建一个DataFrame

import pandas as pd
data = [1,2,3,4,5]
df = pd.DataFrame(data)
print df

   0
0  1
1  2
2  3
3  4
4  5

# 列名分别指定为'Name','Age'
import pandas as pd
data = [['Alex',10],['Bob',12],['Clarke',13]]
df = pd.DataFrame(data,columns=['Name','Age'])
print df

     Name  Age
0    Alex   10
1     Bob   12
2  Clarke   13

5. 从ndarrays/列表字典创建一个DataFrame

# 所有数组长度必须相等
import pandas as pd
data = {'Name':['Tom','Jack','Steve','Ricky'],'Age':[28,34,29,42]}
df = pd.DataFrame(data)
print df

   Age   Name
0   28    Tom
1   34   Jack
2   29  Steve
3   42  Ricky

6. 从字典列表创建一个DataFrame

字典关键字默认作为列名

# 可以看出，c列在索引为first处没有指定值，系统自动设为NaN(Not a Number)
import pandas as pd
data = [{'a':1,'b':2},{'a':5,'b':10,'c':20}]
df = pd.DataFrame(data,index=['first','second'])
print df

        a   b     c
first   1   2   NaN
second  5  10  20.0

# 另一个例子，列表中的两个元素均为字典类型
import pandas as pd
data = [{'a':1,'b':2},{'a':5,'b':10,'c':20}]

# 两个列名称与字典关键字相同
df1 = pd.DataFrame(data,index=['first','second'],columns=['a','b'])
print df1

# 只有一个列名与字典关键字相同，另一个为未知名称
df2 = pd.DataFrame(data,index=['first','second'],columns=['a','b1'])
print df2

        a   b
first   1   2
second  5  10
        a  b1
first   1 NaN
second  5 NaN

7. 从Series字典创建一个DataFrame

import pandas as pd
d = {'one':pd.Series([1,2,3],index=['a','b','c']),
    'two':pd.Series([1,2,3,4],index=['a','b','c','d'])}
df = pd.DataFrame(d)
print df

   one  two
a  1.0    1
b  2.0    2
c  3.0    3
d  NaN    4

8. 列选择

现在开始通过一些实例了解列选择，添加以及删除

# 直接通过列名进行索引
import pandas as pd
d = {'one':pd.Series([1,2,3],index=['a','b','c']),
    'two':pd.Series([1,2,3,4],index=['a','b','c','d'])}
df = pd.DataFrame(d)
print df['one']

a    1.0
b    2.0
c    3.0
d    NaN
Name: one, dtype: float64

9. 列添加

import pandas as pd
d = {'one':pd.Series([1,2,3],index=['a','b','c']),
    'two':pd.Series([1,2,3,4],index=['a','b','c','d'])}
df = pd.DataFrame(d)

# 通过传入一个新的Series到已有的DataFrame中，用来添加新的一列
# 注意这种形式，可以直接对DataFrame新的列名索引进行Series赋值操作
print "Adding a new column by passing as Series:"
df['three'] = pd.Series([10,20,30],index=['a','b','c'])
print df

print "Adding a new column using the existing columns in DataFrame:"
df['four'] = df['one'] + df['three']
print df

Adding a new column by passing as Series:
   one  two  three
a  1.0    1   10.0
b  2.0    2   20.0
c  3.0    3   30.0
d  NaN    4    NaN
Adding a new column using the existing columns in DataFrame:
   one  two  three  four
a  1.0    1   10.0  11.0
b  2.0    2   20.0  22.0
c  3.0    3   30.0  33.0
d  NaN    4    NaN   NaN

10. 列删除

import pandas as pd
d = {'one':pd.Series([1,2,3],index=['a','b','c']),
    'two':pd.Series([1,2,3,4],index=['a','b','c','d']),
    'three':pd.Series([10,20,30],index=['a','b','c'])}
df = pd.DataFrame(d)
print "Our dataframe is:"
print df

# 使用del函数进行列删除操作
print "Deleting the first column using DEL function:"
del df['one']
print df

# 使用pop函数进行列删除操作
print "Deleting another column using POP function:"
df.pop('two')
print df

Our dataframe is:
   one  three  two
a  1.0   10.0    1
b  2.0   20.0    2
c  3.0   30.0    3
d  NaN    NaN    4
Deleting the first column using DEL function:
   three  two
a   10.0    1
b   20.0    2
c   30.0    3
d    NaN    4
Deleting another column using POP function:
   three
a   10.0
b   20.0
c   30.0
d    NaN

11. 行选择，添加和删除

能够通过传递行标签到loc函数选择行

import pandas as pd
d = {'one' : pd.Series([1, 2, 3], index=['a', 'b', 'c']),
     'two' : pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])}
df = pd.DataFrame(d)
print df.loc['b']

one    2.0
two    2.0
Name: b, dtype: float64

可以通过将整数位置传递给iloc函数来选择行

import pandas as pd
d = {'one':pd.Series([1,2,3],index=['a','b','c']),
    'two':pd.Series([1,2,3,4],index=['a','b','c','d'])}
df = pd.DataFrame(d)

print df.iloc[2]

one    3.0
two    3.0
Name: c, dtype: float64

行切片

import pandas as pd
d = {'one':pd.Series([1,2,3],index=['a','b','c']),
    'two':pd.Series([1,2,3,4],index=['a','b','c','d'])}

df = pd.DataFrame(d)
print df[2:4]

   one  two
c  3.0    3
d  NaN    4

添加行

import pandas as pd
df = pd.DataFrame([[1,2],[3,4]],columns=['a','b'])
df2 = pd.DataFrame([[5,6],[7,8]],columns=['a','b'])

df = df.append(df2)
print df

   a  b
0  1  2
1  3  4
0  5  6
1  7  8

删除行

import pandas as pd
df = pd.DataFrame([[1,2],[3,4]],columns=['a','b'])
df2 = pd.DataFrame([[5,6],[7,8]],columns=['a','b'])

print df,'\n'
df = df.append(df2)
print df,'\n'
# 对标签0进行删除，注意标签为0的可能存在多行
df = df.drop(0)
print df

   a  b
0  1  2
1  3  4

   a  b
0  1  2
1  3  4
0  5  6
1  7  8

   a  b
1  3  4
1  7  8

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