import pandas
food_info = pandas.read_csv("food_info.csv")
col_names = food_info.columns.tolist()
print(col_names)
print(food_info.head(3))
['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)']
NDB_No Shrt_Desc Water_(g) Energ_Kcal Protein_(g) \
0 1001 BUTTER WITH SALT 15.87 717 0.85
1 1002 BUTTER WHIPPED WITH SALT 15.87 717 0.85
2 1003 BUTTER OIL ANHYDROUS 0.24 876 0.28
Lipid_Tot_(g) Ash_(g) Carbohydrt_(g) Fiber_TD_(g) Sugar_Tot_(g) ... \
0 81.11 2.11 0.06 0.0 0.06 ...
1 81.11 2.11 0.06 0.0 0.06 ...
2 99.48 0.00 0.00 0.0 0.00 ...
Vit_A_IU Vit_A_RAE Vit_E_(mg) Vit_D_mcg Vit_D_IU Vit_K_(mcg) \
0 2499.0 684.0 2.32 1.5 60.0 7.0
1 2499.0 684.0 2.32 1.5 60.0 7.0
2 3069.0 840.0 2.80 1.8 73.0 8.6
FA_Sat_(g) FA_Mono_(g) FA_Poly_(g) Cholestrl_(mg)
0 51.368 21.021 3.043 215.0
1 50.489 23.426 3.012 219.0
2 61.924 28.732 3.694 256.0
[3 rows x 36 columns]
# print(food_info["Iron_(mg)"])
col = ["Iron_(mg)"]
# print(food_info[col])
div_1000 = food_info["Iron_(mg)"]/1000
print(div_1000)
0 0.00002
1 0.00016
2 0.00000
3 0.00031
4 0.00043
5 0.00050
6 0.00033
7 0.00064
8 0.00016
9 0.00021
10 0.00076
11 0.00007
12 0.00016
13 0.00015
14 0.00013
15 0.00014
16 0.00038
17 0.00044
18 0.00065
19 0.00023
20 0.00052
21 0.00024
22 0.00017
23 0.00013
24 0.00072
25 0.00044
26 0.00020
27 0.00022
28 0.00023
29 0.00041
...
8588 0.00900
8589 0.00030
8590 0.00010
8591 0.00163
8592 0.03482
8593 0.00228
8594 0.00017
8595 0.00017
8596 0.00486
8597 0.00025
8598 0.00023
8599 0.00013
8600 0.00011
8601 0.00068
8602 0.00783
8603 0.00311
8604 0.00030
8605 0.00018
8606 0.00080
8607 0.00004
8608 0.00387
8609 0.00005
8610 0.00038
8611 0.00520
8612 0.00150
8613 0.00140
8614 0.00058
8615 0.00360
8616 0.00350
8617 0.00140
Name: Iron_(mg), Length: 8618, dtype: float64
# It applies the arithmetic operator to the first value in both columns, the second value in both columns,and so on
water_energy = food_info["Water_(g)"]*food_info["Energ_Kcal"]
water_energy = food_info["Water_(g)"]*food_info["Energ_Kcal"]
iron_grams = food_info["Iron_(mg)"]/1000
print(food_info.shape)
food_info["Iron_(g)"] = iron_grams
print(food_info.shape)
(8618, 36)
(8618, 37)
# The largest value in the "Energ_Kcal" column.
max_calories = food_info["Energ_Kcal"].max()
normalized_calories = food_info["Energ_Kcal"]/max_calories
food_info["Normalized_calories"] = normalized_calories
print(food_info.shape)
(8618, 38)
# By default, pandas will sort the data by the column we specify in ascending order and return a new DataFrame
# Sorts the DataFrame in-place, rather tahn returning a new DataFrame
# print(food_info["Sodium_(mg)"])
food_info.sort_values("Sodium_(mg)",inplace=True)
print(food_info["Sodium_(mg)"])
food_info.sort_values("Sodium_(mg)",inplace=True,ascending=False)
print(food_info["Sodium_(mg)"])
760 0.0
610 0.0
611 0.0
8387 0.0
8607 0.0
629 0.0
630 0.0
631 0.0
6470 0.0
654 0.0
8599 0.0
633 0.0
634 0.0
635 0.0
637 0.0
638 0.0
639 0.0
646 0.0
653 0.0
632 0.0
606 0.0
6463 0.0
655 0.0
673 0.0
658 0.0
3636 0.0
659 0.0
660 0.0
661 0.0
3663 0.0
...
8153 NaN
8155 NaN
8156 NaN
8157 NaN
8158 NaN
8159 NaN
8160 NaN
8161 NaN
8163 NaN
8164 NaN
8165 NaN
8167 NaN
8169 NaN
8170 NaN
8172 NaN
8173 NaN
8174 NaN
8175 NaN
8176 NaN
8177 NaN
8178 NaN
8179 NaN
8180 NaN
8181 NaN
8183 NaN
8184 NaN
8185 NaN
8195 NaN
8251 NaN
8267 NaN
Name: Sodium_(mg), Length: 8618, dtype: float64
276 38758.0
5814 27360.0
6192 26050.0
1242 26000.0
1245 24000.0
1243 24000.0
1244 23875.0
292 17000.0
1254 11588.0
5811 10600.0
8575 9690.0
291 8068.0
1249 8031.0
5812 7893.0
1292 7851.0
293 7203.0
4472 7027.0
4836 6820.0
1261 6580.0
3747 6008.0
1266 5730.0
4835 5586.0
4834 5493.0
1263 5356.0
1553 5203.0
1552 5053.0
1251 4957.0
1257 4843.0
294 4616.0
8613 4450.0
...
8153 NaN
8155 NaN
8156 NaN
8157 NaN
8158 NaN
8159 NaN
8160 NaN
8161 NaN
8163 NaN
8164 NaN
8165 NaN
8167 NaN
8169 NaN
8170 NaN
8172 NaN
8173 NaN
8174 NaN
8175 NaN
8176 NaN
8177 NaN
8178 NaN
8179 NaN
8180 NaN
8181 NaN
8183 NaN
8184 NaN
8185 NaN
8195 NaN
8251 NaN
8267 NaN
Name: Sodium_(mg), Length: 8618, dtype: float64