Pandas常用基本操作

编程入门行业动态更新时间:2024-10-09 18:24:28

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Pandas常用基本操作

Pandas基本操作—第一节

以下是实验所用的数据表，需要数据表练习的请留言

本文的所有试验均基于jupyter notebook

使用Pandas读取csv文件并显示

import pandas as pd
#读取csv数据文件
food_info = pd.read_csv('food_info.csv')
#查看数据类型   DataFrame数据类型是pandas的核心数据类型之一
print(type(food_info))
#查看数据
food_info[0:10]  #仅显示前10行

<class 'pandas.core.frame.DataFrame'>

	NDB_No	Shrt_Desc	Water_(g)	Energ_Kcal	Protein_(g)	Lipid_Tot_(g)	Ash_(g)	Carbohydrt_(g)	Sugar_Tot_(g)	...	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)
0	1001	BUTTER WITH SALT	15.87	717	0.85	81.11	2.11	0.06	0.06	...	2499.0	684.0	2.32	1.5	60.0	7.0	51.368	21.021	3.043	215.0
1	1002	BUTTER WHIPPED WITH SALT	15.87	717	0.85	81.11	2.11	0.06	0.06	...	2499.0	684.0	2.32	1.5	60.0	7.0	50.489	23.426	3.012	219.0
2	1003	BUTTER OIL ANHYDROUS	0.24	876	0.28	99.48	0.00	0.00	0.00	...	3069.0	840.0	2.80	1.8	73.0	8.6	61.924	28.732	3.694	256.0
3	1004	CHEESE BLUE	42.41	353	21.40	28.74	5.11	2.34	0.50	...	721.0	198.0	0.25	0.5	21.0	2.4	18.669	7.778	0.800	75.0
4	1005	CHEESE BRICK	41.11	371	23.24	29.68	3.18	2.79	0.51	...	1080.0	292.0	0.26	0.5	22.0	2.5	18.764	8.598	0.784	94.0
5	1006	CHEESE BRIE	48.42	334	20.75	27.68	2.70	0.45	0.45	...	592.0	174.0	0.24	0.5	20.0	2.3	17.410	8.013	0.826	100.0
6	1007	CHEESE CAMEMBERT	51.80	300	19.80	24.26	3.68	0.46	0.46	...	820.0	241.0	0.21	0.4	18.0	2.0	15.259	7.023	0.724	72.0
7	1008	CHEESE CARAWAY	39.28	376	25.18	29.20	3.28	3.06	NaN	...	1054.0	271.0	NaN	NaN	NaN	NaN	18.584	8.275	0.830	93.0
8	1009	CHEESE CHEDDAR	37.10	406	24.04	33.82	3.71	1.33	0.28	...	994.0	263.0	0.78	0.6	24.0	2.9	19.368	8.428	1.433	102.0
9	1010	CHEESE CHESHIRE	37.65	387	23.37	30.60	3.60	4.78	NaN	...	985.0	233.0	NaN	NaN	NaN	NaN	19.475	8.671	0.870	103.0

10 rows × 36 columns

查看读入的csv文件的每一列的数据类型

print(food_info.dtypes)  
#返回每一列的数据类型  最常见的三种int64  float64  object（即string）

NDB_No               int64
Shrt_Desc           object
Water_(g)          float64
Energ_Kcal           int64
Protein_(g)        float64
Lipid_Tot_(g)      float64
Ash_(g)            float64
Carbohydrt_(g)     float64
Fiber_TD_(g)       float64
Sugar_Tot_(g)      float64
Calcium_(mg)       float64
Iron_(mg)          float64
Magnesium_(mg)     float64
Phosphorus_(mg)    float64
Potassium_(mg)     float64
Sodium_(mg)        float64
Zinc_(mg)          float64
Copper_(mg)        float64
Manganese_(mg)     float64
Selenium_(mcg)     float64
Vit_C_(mg)         float64
Thiamin_(mg)       float64
Riboflavin_(mg)    float64
Niacin_(mg)        float64
Vit_B6_(mg)        float64
Vit_B12_(mcg)      float64
Vit_A_IU           float64
Vit_A_RAE          float64
Vit_E_(mg)         float64
Vit_D_mcg          float64
Vit_D_IU           float64
Vit_K_(mcg)        float64
FA_Sat_(g)         float64
FA_Mono_(g)        float64
FA_Poly_(g)        float64
Cholestrl_(mg)     float64
dtype: object

显示前表格的前五行

#显示表格
food_info.head() #默认只显示前5行

	NDB_No	Shrt_Desc	Water_(g)	Energ_Kcal	Protein_(g)	Lipid_Tot_(g)	Ash_(g)	Carbohydrt_(g)	Sugar_Tot_(g)	...	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)
0	1001	BUTTER WITH SALT	15.87	717	0.85	81.11	2.11	0.06	0.06	...	2499.0	684.0	2.32	1.5	60.0	7.0	51.368	21.021	3.043	215.0
1	1002	BUTTER WHIPPED WITH SALT	15.87	717	0.85	81.11	2.11	0.06	0.06	...	2499.0	684.0	2.32	1.5	60.0	7.0	50.489	23.426	3.012	219.0
2	1003	BUTTER OIL ANHYDROUS	0.24	876	0.28	99.48	0.00	0.00	0.00	...	3069.0	840.0	2.80	1.8	73.0	8.6	61.924	28.732	3.694	256.0
3	1004	CHEESE BLUE	42.41	353	21.40	28.74	5.11	2.34	0.50	...	721.0	198.0	0.25	0.5	21.0	2.4	18.669	7.778	0.800	75.0
4	1005	CHEESE BRICK	41.11	371	23.24	29.68	3.18	2.79	0.51	...	1080.0	292.0	0.26	0.5	22.0	2.5	18.764	8.598	0.784	94.0

5 rows × 36 columns

显示前表格的前几行

#当需要显示别的数目的行数时可以给head传参
food_info.head(2)  #显示两行

	NDB_No	Shrt_Desc	Water_(g)	Energ_Kcal	Protein_(g)	Lipid_Tot_(g)	Ash_(g)	Carbohydrt_(g)	Fiber_TD_(g)	Sugar_Tot_(g)	...	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)
0	1001	BUTTER WITH SALT	15.87	717	0.85	81.11	2.11	0.06	0.0	0.06	...	2499.0	684.0	2.32	1.5	60.0	7.0	51.368	21.021	3.043	215.0
1	1002	BUTTER WHIPPED WITH SALT	15.87	717	0.85	81.11	2.11	0.06	0.0	0.06	...	2499.0	684.0	2.32	1.5	60.0	7.0	50.489	23.426	3.012	219.0

2 rows × 36 columns

显示表格的最后五行

#可以查看开头几行，当然也能查看尾几行
food_info.tail()#默认5行

	NDB_No	Shrt_Desc	Water_(g)	Energ_Kcal	Protein_(g)	Lipid_Tot_(g)	Ash_(g)	Carbohydrt_(g)	Sugar_Tot_(g)	...	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)
8613	83110	MACKEREL SALTED	43.00	305	18.50	25.10	13.40	0.00	0.0	...	157.0	47.0	2.38	25.2	1006.0	7.8	7.148	8.320	6.210	95.0
8614	90240	SCALLOP (BAY&SEA) CKD STMD	70.25	111	20.54	0.84	2.97	5.41	0.0	...	5.0	2.0	0.00	0.0	2.0	0.0	0.218	0.082	0.222	41.0
8615	90480	SYRUP CANE	26.00	269	0.00	0.00	0.86	73.14	73.2	...	0.0	0.0	0.00	0.0	0.0	0.0	0.000	0.000	0.000	0.0
8616	90560	SNAIL RAW	79.20	90	16.10	1.40	1.30	2.00	0.0	...	100.0	30.0	5.00	0.0	0.0	0.1	0.361	0.259	0.252	50.0
8617	93600	TURTLE GREEN RAW	78.50	89	19.80	0.50	1.20	0.00	0.0	...	100.0	30.0	0.50	0.0	0.0	0.1	0.127	0.088	0.170	50.0

5 rows × 36 columns

显示表格的最后几行

food_info.tail(2)#只显示最后两行

	NDB_No	Shrt_Desc	Water_(g)	Energ_Kcal	Protein_(g)	Lipid_Tot_(g)	Ash_(g)	Carbohydrt_(g)	Fiber_TD_(g)	Sugar_Tot_(g)	...	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)
8616	90560	SNAIL RAW	79.2	90	16.1	1.4	1.3	2.0	0.0	0.0	...	100.0	30.0	5.0	0.0	0.0	0.1	0.361	0.259	0.252	50.0
8617	93600	TURTLE GREEN RAW	78.5	89	19.8	0.5	1.2	0.0	0.0	0.0	...	100.0	30.0	0.5	0.0	0.0	0.1	0.127	0.088	0.170	50.0

2 rows × 36 columns

查看csv文件的所有列名

#显示列名
print(food_info.columns,type(food_info.columns))

Index(['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)'],dtype='object') <class 'pandas.core.indexes.base.Index'>

查看当前读入的csv文件的数据结构

#查看当前数据的结构多少行多少列
print(food_info.shape)

(8618, 36)

对当前的数据文件进行索引

#pands读入的csv文件也可以进行索引和切片但是要通过一个loc方法
food_info.loc[0]  #只显示第一行也就是csv文件中的列名那一行

NDB_No                         1001
Shrt_Desc          BUTTER WITH SALT
Water_(g)                     15.87
Energ_Kcal                      717
Protein_(g)                    0.85
Lipid_Tot_(g)                 81.11
Ash_(g)                        2.11
Carbohydrt_(g)                 0.06
Fiber_TD_(g)                      0
Sugar_Tot_(g)                  0.06
Calcium_(mg)                     24
Iron_(mg)                      0.02
Magnesium_(mg)                    2
Phosphorus_(mg)                  24
Potassium_(mg)                   24
Sodium_(mg)                     643
Zinc_(mg)                      0.09
Copper_(mg)                       0
Manganese_(mg)                    0
Selenium_(mcg)                    1
Vit_C_(mg)                        0
Thiamin_(mg)                  0.005
Riboflavin_(mg)               0.034
Niacin_(mg)                   0.042
Vit_B6_(mg)                   0.003
Vit_B12_(mcg)                  0.17
Vit_A_IU                       2499
Vit_A_RAE                       684
Vit_E_(mg)                     2.32
Vit_D_mcg                       1.5
Vit_D_IU                         60
Vit_K_(mcg)                       7
FA_Sat_(g)                   51.368
FA_Mono_(g)                  21.021
FA_Poly_(g)                   3.043
Cholestrl_(mg)                  215
Name: 0, dtype: object

对当前的csv文件进行切片操作

#对csv文件数据的切片
food_info.loc[0:5]  #查看前5行与food.head显示的一致

	NDB_No	Shrt_Desc	Water_(g)	Energ_Kcal	Protein_(g)	Lipid_Tot_(g)	Ash_(g)	Carbohydrt_(g)	Sugar_Tot_(g)	...	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)
0	1001	BUTTER WITH SALT	15.87	717	0.85	81.11	2.11	0.06	0.06	...	2499.0	684.0	2.32	1.5	60.0	7.0	51.368	21.021	3.043	215.0
1	1002	BUTTER WHIPPED WITH SALT	15.87	717	0.85	81.11	2.11	0.06	0.06	...	2499.0	684.0	2.32	1.5	60.0	7.0	50.489	23.426	3.012	219.0
2	1003	BUTTER OIL ANHYDROUS	0.24	876	0.28	99.48	0.00	0.00	0.00	...	3069.0	840.0	2.80	1.8	73.0	8.6	61.924	28.732	3.694	256.0
3	1004	CHEESE BLUE	42.41	353	21.40	28.74	5.11	2.34	0.50	...	721.0	198.0	0.25	0.5	21.0	2.4	18.669	7.778	0.800	75.0
4	1005	CHEESE BRICK	41.11	371	23.24	29.68	3.18	2.79	0.51	...	1080.0	292.0	0.26	0.5	22.0	2.5	18.764	8.598	0.784	94.0
5	1006	CHEESE BRIE	48.42	334	20.75	27.68	2.70	0.45	0.45	...	592.0	174.0	0.24	0.5	20.0	2.3	17.410	8.013	0.826	100.0

6 rows × 36 columns

food_info.loc[0:9:2]  #类似Python中的切片，food_info.loc[开始，结束，步长]，这里显示前10行的偶数行

	NDB_No	Shrt_Desc	Water_(g)	Energ_Kcal	Protein_(g)	Lipid_Tot_(g)	Ash_(g)	Carbohydrt_(g)	Sugar_Tot_(g)	...	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)
0	1001	BUTTER WITH SALT	15.87	717	0.85	81.11	2.11	0.06	0.06	...	2499.0	684.0	2.32	1.5	60.0	7.0	51.368	21.021	3.043	215.0
2	1003	BUTTER OIL ANHYDROUS	0.24	876	0.28	99.48	0.00	0.00	0.00	...	3069.0	840.0	2.80	1.8	73.0	8.6	61.924	28.732	3.694	256.0
4	1005	CHEESE BRICK	41.11	371	23.24	29.68	3.18	2.79	0.51	...	1080.0	292.0	0.26	0.5	22.0	2.5	18.764	8.598	0.784	94.0
6	1007	CHEESE CAMEMBERT	51.80	300	19.80	24.26	3.68	0.46	0.46	...	820.0	241.0	0.21	0.4	18.0	2.0	15.259	7.023	0.724	72.0
8	1009	CHEESE CHEDDAR	37.10	406	24.04	33.82	3.71	1.33	0.28	...	994.0	263.0	0.78	0.6	24.0	2.9	19.368	8.428	1.433	102.0

5 rows × 36 columns

通过列索引取数据

#通过索引可以按行取数据，当然也可以通过列索引来取整列的数据
food_info['NDB_No'][0:5]  #只显示NDB_No这一列的前5条数据

0    1001
1    1002
2    1003
3    1004
4    1005
Name: NDB_No, dtype: int64

获取所有以g结尾的列名

#现在有一个需求要获取所有以g结尾的列名
columns = food_info.columns #这里得到的columns并不是list而是<class 'pandas.core.indexes.base.Index'>可以使用tolist方法转为list类型
print(columns.tolist(),type(columns.tolist()))
print('*'*100)
g_columns = []
for this_column in columns:if this_column.endswith('(g)'):g_columns.append(this_column)
print(g_columns)
food_info[g_columns][0:5]  #所有以g结尾的有29列  只显示前5行

['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)'] <class 'list'>
****************************************************************************************************
['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)	Lipid_Tot_(g)	Ash_(g)	Carbohydrt_(g)	Sugar_Tot_(g)	FA_Sat_(g)	FA_Mono_(g)	FA_Poly_(g)
0	315.87	0.85	81.11	2.11	0.06	0.06	51.368	21.021	3.043
1	315.87	0.85	81.11	2.11	0.06	0.06	50.489	23.426	3.012
2	300.24	0.28	99.48	0.00	0.00	0.00	61.924	28.732	3.694
3	342.41	21.40	28.74	5.11	2.34	0.50	18.669	7.778	0.800
4	341.11	23.24	29.68	3.18	2.79	0.51	18.764	8.598	0.784

Pandas对csv文件的基本数学运算

#针对数据的基本数学运算 +|-|×|÷  与常数运算每一个值与常数运算，列与列运算，列的对应位置运算

#将Iron_(mg)这一列的数据转换成以g为单位
(food_info['Iron_(mg)']/1000)[0:5]  #只显示前五条数据

0    0.00002
1    0.00016
2    0.00000
3    0.00031
4    0.00043
Name: Iron_(mg), dtype: float64

#将Protein_(g)这一列的值全部加100
food_info['Protein_(g)'] += 100
food_info['Protein_(g)'][0:5]  #只显示前五行

0    100.85
1    100.85
2    100.28
3    121.40
4    123.24
Name: Protein_(g), dtype: float64

#将Lipid_Tot_(g)这一列的值全部减去10
food_info['Lipid_Tot_(g)'] -= 10
food_info['Lipid_Tot_(g)'][0:5]  #只显示前五行

0    71.11
1    71.11
2    89.48
3    18.74
4    19.68
Name: Lipid_Tot_(g), dtype: float64

#将water_(g)与Protein_(g)相乘
new_lipid = food_info['Lipid_Tot_(g)'] * food_info['Carbohydrt_(g)']
new_lipid[0:5]

0     4.2666
1     4.2666
2     0.0000
3    43.8516
4    54.9072
dtype: float64

按某一列的值排序操作

food_info.sort_values('Water_(g)',inplace=True)  #inplace = True不生成新的dataframe
food_info.sort_values('Water_(g)',inplace=False)[0:5]#inplace = False,默认为false生成新的dataframe只显示前5行

	NDB_No	Shrt_Desc	Water_(g)	Energ_Kcal	Lipid_Tot_(g)	...	Vit_E_(mg)	Vit_D_mcg	Vit_D_IU	Vit_K_(mcg)	FA_Sat_(g)	FA_Mono_(g)	FA_Poly_(g)	Cholestrl_(mg)
676	4544	SHORTENING HOUSEHOLD LARD&VEG OIL	400.0	900	90.0	...	1.0	0.0	0.0	21.5	40.3	44.4	10.9	56.0
664	4520	FAT MUTTON TALLOW	400.0	902	90.0	...	2.8	0.7	28.0	0.0	47.3	40.6	7.8	102.0
665	4528	OIL WALNUT	400.0	884	90.0	...	0.4	0.0	0.0	15.0	9.1	22.8	63.3	0.0
666	4529	OIL ALMOND	400.0	884	90.0	...	39.2	0.0	0.0	7.0	8.2	69.9	17.4	0.0
667	4530	OIL APRICOT KERNEL	400.0	884	90.0	...	4.0	NaN	NaN	NaN	6.3	60.0	29.3	0.0

5 rows × 36 columns

按某一列的值索引值排序返回索引与列值

food_info['Water_(g)'].sort_index(ascending=False)[0:5]  #对某一列索引值的排序  只显示前五行

8617    478.50
8616    479.20
8615    426.00
8614    470.25
8613    443.00
Name: Water_(g), dtype: float64

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Pandas常用基本操作

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常用操作 Pandas

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Pandas常用基本操作

Pandas常用基本操作

Pandas基本操作—第一节

使用Pandas读取csv文件并显示

查看读入的csv文件的每一列的数据类型

显示前表格的前五行

显示前表格的前几行

显示表格的最后五行

显示表格的最后几行

查看csv文件的所有列名

查看当前读入的csv文件的数据结构

对当前的数据文件进行索引

对当前的csv文件进行切片操作

通过列索引取数据

获取所有以g结尾的列名

Pandas对csv文件的基本数学运算

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