Pandas数据读取与显示2

import pandas as pd food_info = pd.read_csv("D:\\test\\food_info.csv") #此处需要转义 print (type(food_info)) first_row = food_info.head() #默认是前5行 print (food_info.shape) (8618, 36) print (food_info.loc[0]) #读取第一行 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 food_info.loc[1:3]#不用print就会以表格显示，并且与python不一样的的是第三行也显示了 NDB_NoShrt_DescWater_(g)Energ_KcalProtein_(g)Lipid_Tot_(g)Ash_(g)Carbohydrt_(g)Fiber_TD_(g)Sugar_Tot_(g)…Vit_A_IUVit_A_RAEVit_E_(mg)Vit_D_mcgVit_D_IUVit_K_(mcg)FA_Sat_(g)FA_Mono_(g)FA_Poly_(g)Cholestrl_(mg)11002BUTTER WHIPPED WITH SALT15.877170.8581.112.110.060.00.06…2499.0684.02.321.560.07.050.48923.4263.012219.021003BUTTER OIL ANHYDROUS0.248760.2899.480.000.000.00.00…3069.0840.02.801.873.08.661.92428.7323.694256.031004CHEESE BLUE42.4135321.4028.745.112.340.00.50…721.0198.00.250.521.02.418.6697.7780.80075.0

3 rows × 36 columns

label = [2,5,10] food_info.loc[label] #只显示2，5，10行 NDB_NoShrt_DescWater_(g)Energ_KcalProtein_(g)Lipid_Tot_(g)Ash_(g)Carbohydrt_(g)Fiber_TD_(g)Sugar_Tot_(g)…Vit_A_IUVit_A_RAEVit_E_(mg)Vit_D_mcgVit_D_IUVit_K_(mcg)FA_Sat_(g)FA_Mono_(g)FA_Poly_(g)Cholestrl_(mg)21003BUTTER OIL ANHYDROUS0.248760.2899.480.000.000.00.00…3069.0840.02.801.873.08.661.92428.7323.694256.051006CHEESE BRIE48.4233420.7527.682.700.450.00.45…592.0174.00.240.520.02.317.4108.0130.826100.0101011CHEESE COLBY38.2039423.7632.113.362.570.00.52…994.0264.00.280.624.02.720.2189.2800.95395.0

3 rows × 36 columns

colums = ['Shrt_Desc','NDB_No'] food_info[colums] #指定找某些列 Shrt_DescNDB_No0BUTTER WITH SALT10011BUTTER WHIPPED WITH SALT10022BUTTER OIL ANHYDROUS10033CHEESE BLUE10044CHEESE BRICK10055CHEESE BRIE10066CHEESE CAMEMBERT10077CHEESE CARAWAY10088CHEESE CHEDDAR10099CHEESE CHESHIRE101010CHEESE COLBY101111CHEESE COTTAGE CRMD LRG OR SML CURD101212CHEESE COTTAGE CRMD W/FRUIT101313CHEESE COTTAGE NONFAT UNCRMD DRY LRG OR SML CURD101414CHEESE COTTAGE LOWFAT 2% MILKFAT101515CHEESE COTTAGE LOWFAT 1% MILKFAT101616CHEESE CREAM101717CHEESE EDAM101818CHEESE FETA101919CHEESE FONTINA102020CHEESE GJETOST102121CHEESE GOUDA102222CHEESE GRUYERE102323CHEESE LIMBURGER102424CHEESE MONTEREY102525CHEESE MOZZARELLA WHL MILK102626CHEESE MOZZARELLA WHL MILK LO MOIST102727CHEESE MOZZARELLA PART SKIM MILK102828CHEESE MOZZARELLA LO MOIST PART-SKIM102929CHEESE MUENSTER1030………8588BABYFOOD CRL RICE W/ PEARS & APPL DRY INST435448589BABYFOOD BANANA NO TAPIOCA STR435468590BABYFOOD BANANA APPL DSSRT STR435508591SNACKS TORTILLA CHIPS LT (BAKED W/ LESS OIL)435668592CEREALS RTE POST HONEY BUNCHES OF OATS HONEY RSTD435708593POPCORN MICROWAVE LOFAT&NA435728594BABYFOOD FRUIT SUPREME DSSRT435858595CHEESE SWISS LOW FAT435898596BREAKFAST BAR CORN FLAKE CRUST W/FRUIT435958597CHEESE MOZZARELLA LO NA435978598MAYONNAISE DRSNG NO CHOL435988599OIL CORN PEANUT AND OLIVE440058600SWEETENERS TABLETOP FRUCTOSE LIQ440188601CHEESE FOOD IMITATION440488602CELERY FLAKES DRIED440558603PUDDINGS CHOC FLAVOR LO CAL INST DRY MIX440618604BABYFOOD GRAPE JUC NO SUGAR CND440748605JELLIES RED SUGAR HOME PRESERVED441108606PIE FILLINGS BLUEBERRY CND441588607COCKTAIL MIX NON-ALCOHOLIC CONCD FRZ442038608PUDDINGS CHOC FLAVOR LO CAL REG DRY MIX442588609PUDDINGS ALL FLAVORS XCPT CHOC LO CAL REG DRY MIX442598610PUDDINGS ALL FLAVORS XCPT CHOC LO CAL INST DRY…442608611VITAL WHEAT GLUTEN480528612FROG LEGS RAW802008613MACKEREL SALTED831108614SCALLOP (BAY&SEA) CKD STMD902408615SYRUP CANE904808616SNAIL RAW905608617TURTLE GREEN RAW93600

8618 rows × 2 columns

print (food_info.dtypes) #显示每一列的数据类型 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 col_names = food_info.columns.tolist() #转化成列表 gram_names = [] for c in col_names: if c.endswith(('(g)')): #以(g)结尾的挑选处理 gram_names.append(c) gram_df = food_info[gram_names] print (gram_df.head(3)) Water_(g) Protein_(g) Lipid_Tot_(g) Ash_(g) Carbohydrt_(g) \ 0 15.87 0.85 81.11 2.11 0.06 1 15.87 0.85 81.11 2.11 0.06 2 0.24 0.28 99.48 0.00 0.00 Fiber_TD_(g) Sugar_Tot_(g) FA_Sat_(g) FA_Mono_(g) FA_Poly_(g) 0 0.0 0.06 51.368 21.021 3.043 1 0.0 0.06 50.489 23.426 3.012 2 0.0 0.00 61.924 28.732 3.694