Python数据分析之Pandas（数据结构）

时间:2022-07-22
本文章向大家介绍Python数据分析之Pandas（数据结构），主要内容包括其使用实例、应用技巧、基本知识点总结和需要注意事项，具有一定的参考价值，需要的朋友可以参考一下。

Pandas数据结构

Series

Series是一维的数据结构。

通过list构建Series

#导入pandas
import pandas as pd
ser_obj =pd.Series(range(10,15))
print(type(ser_obj)) # <class 'pandas.core.series.Series'>
print(ser_obj)
<class 'pandas.core.series.Series'>
0    10
1    11
2    12
3    13
4    14
dtype: int32

获取数据

print(type(ser_obj.values)) # <class 'numpy.ndarray'>
print(ser_obj.values) # [10 11 12 13 14]
<class 'numpy.ndarray'>
[10 11 12 13 14]

获取索引

print(type(ser_obj.index)) # <class 'pandas.core.indexes.range.RangeIndex'>
print(ser_obj.index) # RangeIndex(start=0, stop=5, step=1)
<class 'pandas.core.indexes.range.RangeIndex'>
RangeIndex(start=0, stop=5, step=1)

注意索引对象不可变

# 索引对象不可变
ser_obj.index[0] = 2
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-53-ce46badf9dd7> in <module>()
----> 1 ser_obj.index[0] = 2
G:Anaconda3libsite-packagespandascoreindexesbase.py in __setitem__(self, key, value)
   1668
   1669     def __setitem__(self, key, value):
-> 1670         raise TypeError("Index does not support mutable operations")
   1671
   1672     def __getitem__(self, key):
TypeError: Index does not support mutable operations

预览数据

print(ser_obj.head(3))
0    10
1    11
2    12
dtype: int32

通过索引获取数据

print(ser_obj[0]) # 10
10

索引与数据的对应关系仍保持在数组运算的结果中

print(ser_obj > 12)
print(ser_obj[ser_obj > 12])
0    False
1    False
2    False
3     True
4     True
dtype: bool
3    13
4    14
dtype: int32

整合代码

# 通过list构建Series
ser_obj =pd.Series(range(10,15))
print(type(ser_obj)) # <class 'pandas.core.series.Series'>
print(ser_obj)

# 获取数据
print(type(ser_obj.values)) # <class 'numpy.ndarray'>
print(ser_obj.values) # [10 11 12 13 14]

# 获取索引
print(type(ser_obj.index)) # <class 'pandas.core.indexes.range.RangeIndex'>
print(ser_obj.index) # RangeIndex(start=0, stop=5, step=1)

# 预览数据
print(ser_obj.head(3))

#通过索引获取数据
print(ser_obj[0]) # 10

# 索引与数据的对应关系仍保持在数组运算的结果中
print(ser_obj > 12)
print(ser_obj[ser_obj > 12])
<class 'pandas.core.series.Series'>
0    10
1    11
2    12
3    13
4    14
dtype: int32
<class 'numpy.ndarray'>
[10 11 12 13 14]
<class 'pandas.core.indexes.range.RangeIndex'>
RangeIndex(start=0, stop=5, step=1)
0    10
1    11
2    12
dtype: int32
10
0    False
1    False
2    False
3     True
4     True
dtype: bool
3    13
4    14
dtype: int32

通过dict构建Series(注意：字典的key自动作为索引)

year_data = {2001: 17.8, 2002: 20.1, 2003: 16.5}
ser_obj2 = pd.Series(year_data)
print(type(ser_obj2)) # <class 'pandas.core.series.Series'>
print(ser_obj2)
<class 'pandas.core.series.Series'>
2001    17.8
2002    20.1
2003    16.5
dtype: float64

获取数据

print(type(ser_obj2.values)) # <class 'numpy.ndarray'>
print(ser_obj2.values) # [ 17.8  20.1  16.5]
<class 'numpy.ndarray'>
[ 17.8  20.1  16.5]

获取索引

print(type(ser_obj2.index)) # <class 'pandas.core.indexes.numeric.Int64Index'>
print(ser_obj2.index) # Int64Index([2001, 2002, 2003], dtype='int64')
<class 'pandas.core.indexes.numeric.Int64Index'>
Int64Index([2001, 2002, 2003], dtype='int64')

预览数据（head()不加参数则显示全部）

print(ser_obj2.head())
2001    17.8
2002    20.1
2003    16.5
dtype: float64

通过索引获取数据

print(ser_obj2[2001]) # 17.8
17.8

整合代码

# 通过dict构建Series(注意：字典的key自动作为索引)
year_data = {2001: 17.8, 2002: 20.1, 2003: 16.5}
ser_obj2 = pd.Series(year_data)
print(type(ser_obj2)) # <class 'pandas.core.series.Series'>
print(ser_obj2)

# 获取数据
print(type(ser_obj2.values)) # <class 'numpy.ndarray'>
print(ser_obj2.values) # [ 17.8  20.1  16.5]

# 获取索引
print(type(ser_obj2.index)) # <class 'pandas.core.indexes.numeric.Int64Index'>
print(ser_obj2.index) # Int64Index([2001, 2002, 2003], dtype='int64')

# 预览数据（head()不加参数则显示全部）
print(ser_obj2.head())

#通过索引获取数据
print(ser_obj2[2001]) # 17.8
<class 'pandas.core.series.Series'>
2001    17.8
2002    20.1
2003    16.5
dtype: float64
<class 'numpy.ndarray'>
[ 17.8  20.1  16.5]
<class 'pandas.core.indexes.numeric.Int64Index'>
Int64Index([2001, 2002, 2003], dtype='int64')
2001    17.8
2002    20.1
2003    16.5
dtype: float64
17.8

DataFrame

一个Dataframe就是一张表格，Series表示的是一维数组，Dataframe则是一个二维数组，可以类比成一张excel的spreadsheet。也可以把 Dataframe当做一组Series的集合。

通过ndarray构建DataFrame

import numpy as np

# 通过ndarray构建DataFrame
array = np.random.randn(5,4)
print(array)

df_obj = pd.DataFrame(array)
print(df_obj.head())
[[ 0.7346628  -1.13733651  0.72853785  0.38743511]
 [ 0.49549724  3.96998008  1.13567695 -0.21425912]
 [ 0.22094222  0.7766603   0.46086182  0.33199643]
 [-0.46279419  0.85898771  0.41993259 -0.61997791]
 [-0.83296535  1.19450707 -1.45531366 -0.13990243]]
          0         1         2         3
0  0.734663 -1.137337  0.728538  0.387435
1  0.495497  3.969980  1.135677 -0.214259
2  0.220942  0.776660  0.460862  0.331996
3 -0.462794  0.858988  0.419933 -0.619978
4 -0.832965  1.194507 -1.455314 -0.139902

通过dict构建DataFrame

dict_data = {'A': 1.,
             'B': pd.Timestamp('20180316'),
             'C': pd.Series(1, index=list(range(4)),dtype='float32'),
             'D': np.array([3] * 4,dtype='int32'),
             'E' : pd.Categorical(["Python","Java","C++","C#"])
            }
print(dict_data)
df_obj2 = pd.DataFrame(dict_data)
print(df_obj2.head())
{'A': 1.0, 'B': Timestamp('2018-03-16 00:00:00'), 'C': 0    1.0
1    1.0
2    1.0
3    1.0
dtype: float32, 'D': array([3, 3, 3, 3]), 'E': [Python, Java, C++, C#]
Categories (4, object): [C#, C++, Java, Python]}
     A          B    C  D       E
0  1.0 2018-03-16  1.0  3  Python
1  1.0 2018-03-16  1.0  3    Java
2  1.0 2018-03-16  1.0  3     C++
3  1.0 2018-03-16  1.0  3      C#

通过列索引获取列数据

print(df_obj2['A'])
print(type(df_obj2['A']))

print(df_obj2.A)
0    1.0
1    1.0
2    1.0
3    1.0
Name: A, dtype: float64
<class 'pandas.core.series.Series'>
0    1.0
1    1.0
2    1.0
3    1.0
Name: A, dtype: float64

通过行索引(.loc)获取行数据

print(df_obj2.loc[0])
print(type(df_obj2.loc[0]))
A                      1
B    2018-03-16 00:00:00
C                      1
D                      3
E                 Python
Name: 0, dtype: object
<class 'pandas.core.series.Series'>

增加列

df_obj2['F'] = df_obj2['D'] + 4
print(df_obj2.head())
     A          B    C  D       E  F
0  1.0 2018-03-16  1.0  3  Python  7
1  1.0 2018-03-16  1.0  3    Java  7
2  1.0 2018-03-16  1.0  3     C++  7
3  1.0 2018-03-16  1.0  3      C#  7

删除列

del(df_obj2['F'] )
print(df_obj2.head())
     A          B    C  D       E
0  1.0 2018-03-16  1.0  3  Python
1  1.0 2018-03-16  1.0  3    Java
2  1.0 2018-03-16  1.0  3     C++
3  1.0 2018-03-16  1.0  3      C#

整合代码

import numpy as np

# 通过ndarray构建DataFrame
array = np.random.randn(5,4)
print(array)

# 通过dict构建DataFrame
df_obj = pd.DataFrame(array)
print(df_obj.head())

dict_data = {'A': 1.,
             'B': pd.Timestamp('20180316'),
             'C': pd.Series(1, index=list(range(4)),dtype='float32'),
             'D': np.array([3] * 4,dtype='int32'),
             'E' : pd.Categorical(["Python","Java","C++","C#"])
            }
print(dict_data)
df_obj2 = pd.DataFrame(dict_data)
print(df_obj2.head())

# 通过列索引获取列数据
print(df_obj2['A'])
print(type(df_obj2['A']))

print(df_obj2.A)

# 通过行索引获取行数据
print(df_obj2.loc[0])
print(type(df_obj2.loc[0]))

# 增加列
df_obj2['G'] = df_obj2['D'] + 4
print(df_obj2.head())

# 删除列
del(df_obj2['G'] )
print(df_obj2.head())
[[ 0.23758715 -1.13751056 -0.0863061  -0.71309414]
 [ 0.08129935  1.32099551 -0.27057527  0.49270974]
 [ 0.96111551  1.08307556  1.5094844   0.96117055]
 [-0.31003598  1.33959047 -0.42150857 -1.20605423]
 [ 0.12655879 -1.01810288 -1.34025171  0.98758417]]
          0         1         2         3
0  0.237587 -1.137511 -0.086306 -0.713094
1  0.081299  1.320996 -0.270575  0.492710
2  0.961116  1.083076  1.509484  0.961171
3 -0.310036  1.339590 -0.421509 -1.206054
4  0.126559 -1.018103 -1.340252  0.987584
{'A': 1.0, 'B': Timestamp('2018-03-16 00:00:00'), 'C': 0    1.0
1    1.0
2    1.0
3    1.0
dtype: float32, 'D': array([3, 3, 3, 3]), 'E': [Python, Java, C++, C#]
Categories (4, object): [C#, C++, Java, Python]}
     A          B    C  D       E
0  1.0 2018-03-16  1.0  3  Python
1  1.0 2018-03-16  1.0  3    Java
2  1.0 2018-03-16  1.0  3     C++
3  1.0 2018-03-16  1.0  3      C#
0    1.0
1    1.0
2    1.0
3    1.0
Name: A, dtype: float64
<class 'pandas.core.series.Series'>
0    1.0
1    1.0
2    1.0
3    1.0
Name: A, dtype: float64
A                      1
B    2018-03-16 00:00:00
C                      1
D                      3
E                 Python
Name: 0, dtype: object
<class 'pandas.core.series.Series'>
     A          B    C  D       E  G
0  1.0 2018-03-16  1.0  3  Python  7
1  1.0 2018-03-16  1.0  3    Java  7
2  1.0 2018-03-16  1.0  3     C++  7
3  1.0 2018-03-16  1.0  3      C#  7
     A          B    C  D       E
0  1.0 2018-03-16  1.0  3  Python
1  1.0 2018-03-16  1.0  3    Java
2  1.0 2018-03-16  1.0  3     C++
3  1.0 2018-03-16  1.0  3      C#

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