【python学习】Python数据处理库pandas

╰半橙微兮° 2022-04-22 09:58 375阅读 0赞

版本信息

Python                   3.6.2

pandas                 0.23.4

本文所需文件下载地址：[https://download.csdn.net/download/plsong\_csdn/10764054][https_download.csdn.net_download_plsong_csdn_10764054]

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# 基本操作 #

**读取文件**

mport pandas as pd
    food_info = pd.read_csv('food_info.csv') #读取数据
    print(type(food_info)) #DataFrame结构
    print(food_info.dtypes) #当前数据中的数据结构有哪些
    food_info.head() #显示前几个信息
    food_info.tail() #显示后几个信息
    food_info.columns #每个列的指标，也就是说猎命
    food_info.shape #样本的大小

运行结果：

<class 'pandas.core.frame.DataFrame'>  
NDB\_No               int64  
Shrt\_Desc           object  
Water\_(g)          float64  
Energ\_Kcal           int64  
......  
Cholestrl\_(mg)     float64  
dtype: object

说明：

（1）DataFrame数据类型是pd读取文件进行存储的数据类型，可以近似看作一个矩阵的结构。

（2）object数据类型其实就是string类型的数据。

--------------------

# pandas索引 #

**按行按列读取**

import pandas as pd
    food_info = pd.read_csv('food_info.csv') #读取数据
    print(food_info.loc[0]) #取第一行数据
    print(food_info.loc[3:6]) #取4:7行的数据
    ndb_col = food_info['NDB_No'] #取'NDB_No'这一列的数据
    print(ndb_col)
    colums = ['NDB_No', 'Water_(g)'] #同时读取'NDB_No'， 'Water_(g)'这一列的数据
    print(food_info[colums])

运行结果：

NDB\_No                         1001  
Shrt\_Desc          BUTTER WITH SALT  
......  
FA\_Poly\_(g)                   3.043  
Cholestrl\_(mg)                  215  
Name: 0, dtype: object

NDB\_No         Shrt\_Desc       ...        FA\_Poly\_(g)  Cholestrl\_(mg)  
3    1004       CHEESE BLUE       ...              0.800            75.0  
4    1005      CHEESE BRICK       ...              0.784            94.0  
5    1006       CHEESE BRIE       ...              0.826           100.0  
6    1007  CHEESE CAMEMBERT       ...              0.724            72.0

\[4 rows x 36 columns\]  
0        1001  
1        1002  
2        1003  
  
        ...    
  
8617    93600  
Name: NDB\_No, Length: 8618, dtype: int64

NDB\_No  Water\_(g)  
0       1001      15.87  
1       1002      15.87  
......  
8617   93600      78.50

\[8618 rows x 2 columns\]

**读取文件中的特定信息**

import pandas as pd
    food_info = pd.read_csv('food_info.csv') #读取数据
    col_name = food_info.columns.tolist() #将当前的列名作为一个list
    print(col_name)
    print('---------------------')
    gram_columns = []
    
    for c in col_name:
        if c.endswith('(g)'):
            gram_columns.append(c)
    print(gram_columns)
    print('---------------------')
    gram_df = food_info[gram_columns] #frame结构
    print(gram_df.head(3))

**运行结果：**

\['NDB\_No', 'Shrt\_Desc', 'Water\_(g)', 'Energ\_Kcal', 'Protein\_(g)', 'Lipid\_Tot\_(g)', 'Ash\_(g)', 'Carbohydrt\_(g)', 'Fiber\_TD\_(g)', 'Sugar\_Tot\_(g)', 'Calcium\_(mg)', 'Iron\_(mg)', 'Magnesium\_(mg)', 'Phosphorus\_(mg)', 'Potassium\_(mg)', 'Sodium\_(mg)', 'Zinc\_(mg)', 'Copper\_(mg)', 'Manganese\_(mg)', 'Selenium\_(mcg)', 'Vit\_C\_(mg)', 'Thiamin\_(mg)', 'Riboflavin\_(mg)', 'Niacin\_(mg)', 'Vit\_B6\_(mg)', 'Vit\_B12\_(mcg)', 'Vit\_A\_IU', 'Vit\_A\_RAE', 'Vit\_E\_(mg)', 'Vit\_D\_mcg', 'Vit\_D\_IU', 'Vit\_K\_(mcg)', 'FA\_Sat\_(g)', 'FA\_Mono\_(g)', 'FA\_Poly\_(g)', 'Cholestrl\_(mg)'\]  
\-----------------------  
\['Water\_(g)', 'Protein\_(g)', 'Lipid\_Tot\_(g)', 'Ash\_(g)', 'Carbohydrt\_(g)', 'Fiber\_TD\_(g)', 'Sugar\_Tot\_(g)', 'FA\_Sat\_(g)', 'FA\_Mono\_(g)', 'FA\_Poly\_(g)'\]  
\-----------------------  
   Water\_(g)  Protein\_(g)     ...       FA\_Mono\_(g)  FA\_Poly\_(g)  
0      15.87         0.85     ...            21.021        3.043  
1      15.87         0.85     ...            23.426        3.012  
2       0.24         0.28     ...            28.732        3.694

\[3 rows x 10 columns\]

--------------------

# 添加新属性 #

import pandas as pd
    food_info = pd.read_csv('food_info.csv') #读取数据
    iron_grams = food_info['Iron_(mg)'] / 1000 #将mg化为g
    print(food_info.shape)
    food_info['Iron_(g)'] = iron_grams #将['Iron_(g)']这一列属性添加到food_info这个frame里面
    print(food_info.shape)
    food_info.head(4)

**运行结果：**

(8618, 36)  
(8618, 37)

**说明：**

food_info['Iron_(g)'] = iron_grams

上面这一句代码其实做了两个工作

（1）为food\_info创建一个新属性，也就是说添加了新的一列

（2）将\['Iron\_(g)'\]这个新属性赋值给food\_info

--------------------

# 对某一属性进行排序 #

import pandas as pd
    food_info = pd.read_csv('food_info.csv') #读取数据
    food_info.sort_values('Water_(g)',inplace=True)
    print(food_info['Water_(g)'])
    print('--------------------------------------')
    food_info.sort_values('Water_(g)',inplace=True, ascending=False) #取消升序
    print(food_info['Water_(g)'])

运行结果：

676       0.00  
664       0.00  
         ...    
4404     99.98  
4372     99.98  
4378    100.00  
4377    100.00  
4348    100.00  
4376    100.00  
4209    100.00  
1983       NaN  
6067       NaN  
6095       NaN  
6113       NaN  
6150       NaN  
7776       NaN  
Name: Water\_(g), Length: 8618, dtype: float64  
\--------------------------------------  
4209    100.00  
4376    100.00  
4348    100.00  
4377    100.00  
  
  
         ...    
  
743       0.00  
676       0.00  
1983       NaN  
6067       NaN  
6095       NaN  
6113       NaN  
6150       NaN  
7776       NaN  
Name: Water\_(g), Length: 8618, dtype: float64

说明：NaN代表该列是缺失的。

--------------------

# 求泰坦尼克号上乘客的平均年龄 #

**方法一**

import pandas as pd
    import numpy as np
    
    titanic_survival = pd.read_csv('titanic_train.csv')
    age = titanic_survival['Age'] #读取年龄
    print('age[0:10]:\n',age.loc[0:10]) #输出age的0:10列
    age_is_null = pd.isnull(age) #判断是否有空缺项
    good_ages = titanic_survival["Age"][age_is_null == False]
    age_mean = sum(good_ages) / len(good_ages)
    print("age_mean:",age_mean)

**运行结果：**

age\[0:10\]:  
 0     22.0  
1     38.0  
2     26.0  
3     35.0  
4     35.0  
5      NaN  
6     54.0  
7      2.0  
8     27.0  
9     14.0  
10     4.0  
Name: Age, dtype: float64  
age\_mean: 29.69911764705882

说明：因为有空缺的项会对结果有影响，因此，计算时应当对空缺的项目进行处理，上面采取的是去掉年龄空缺的乘客；

**方法二：**

import pandas as pd
    import numpy as np
    
    titanic_survival = pd.read_csv('titanic_train.csv')
    age_mean = titanic_survival['Age'].mean()
    print('age_mean:',age_mean)

运行结果：

age\_mean: 29.69911764705882

说明：

（1）用mean()函数，采取默认去掉年龄空缺的乘客。.

（2）去掉有空缺的行，可以使用dropna()

--------------------

# 两个属性之间的关系pivot\_table #

**示例：求不同船舱的平均票价**

import pandas as pd
    import numpy as np
    
    titanic_survival = pd.read_csv('titanic_train.csv')
    fares_by_calss = titanic_survival.pivot_table(index='Pclass',values="Fare",aggfunc=np.mean)
    print(fares_by_calss)

运行结果：

Pclass      Fare        
1         84.154687  
2         20.662183  
3         13.675550

**示例：求不同船舱的乘客的平均年龄**

import pandas as pd
    import numpy as np
    
    titanic_survival = pd.read_csv('titanic_train.csv')
    fares_by_calss = titanic_survival.pivot_table(index='Pclass',values="Age",aggfunc=np.mean)
    print(fares_by_calss)

**运行结果**：

Age  
Pclass             
1       38.233441  
2       29.877630  
3       25.140620

**示例：一个量与其他两个量之间的关系。求不同上船地点乘客的幸存总数和票价总数**

import pandas as pd
    import numpy as np
    
    titanic_survival = pd.read_csv('titanic_train.csv')
    fares_by_calss = titanic_survival.pivot_table(index='Embarked',values=["Age","Survived"],aggfunc=np.sum)
    print(fares_by_calss)

**运行结果：**

Age  Survived  
Embarked                      
C          4005.92        93  
Q           786.50        30  
S         16312.75       217

--------------------

# pandas中的series结构 #

**初识series**

import pandas as pd
    import numpy as np
    
    fandango = pd.read_csv('fandango_score_comparison.csv')
    series_film = fandango['FILM']
    print(type(series_film))
    print(series_film[0:5])
    
    film_name = series_film.values
    print(type(film_name))

运行结果：

<class 'pandas.core.series.Series'>  
0    Avengers: Age of Ultron (2015)  
1                 Cinderella (2015)  
2                    Ant-Man (2015)  
3            Do You Believe? (2015)  
4     Hot Tub Time Machine 2 (2015)  
Name: FILM, dtype: object

说明：

(1)series可以看作是datafram的组成部分。有众多series组成dataframe.

(2)series又是由ndarray构成

**series索引**

import pandas as pd
    from pandas import Series
    
    fandango = pd.read_csv('fandango_score_comparison.csv') #读取文件，dataframe
    series_film = fandango['FILM'] #series
    series_rt = fandango['RottenTomatoes'] #series
    film_name = series_film.values #ndarray
    rt_scores = series_rt.values #ndarray
    series_custom = Series(rt_scores, index=film_name)
    
    print(series_custom[['Minions (2015)', 'Leviathan (2014)']])

运行结果：

Minions (2015)      54  
Leviathan (2014)    99  
dtype: int64

说明

series_custom = Series(rt_scores, index=film_name)

series可以用名字当索引

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★finished 2018.11.4night

★by songpl

[https_download.csdn.net_download_plsong_csdn_10764054]: https://download.csdn.net/download/plsong_csdn/10764054