2021-01-30
阿新 • • 發佈：2021-01-31
'''
練習1
• 讀取北向.csv 指定 trade_date 為 行索引
• 檢視資料的基本資訊 有無缺失值 對其缺失值進行處理
• 刪除缺失值所在行
• 檢視資料的基本資訊 檢視資料是否清洗完畢
• 標籤為 index 這列沒啥用 將該列刪除
• 觀察資料是否有重複行
• 將重複行進行刪除
• 將行索引 進行升序
• 將處理好的資料 儲存至 北向(副).csv
'''
import numpy as np import pandas as pd data = pd.read_csv(r"北向.csv") print(data) ''' index trade_date ggt_ss ggt_sz hgt sgt north_money south_money 0 0 20190624 -541.17 792.38 -757.96 -1153.14 -1911.10 251.21 1 1 20190621 -97.40 701.36 3722.36 3608.14 7330.50 603.96 2 2 20190620 660.05 555.23 1914.44 3650.47 5564.91 1215.28 3 3 20190619 -491.58 186.47 2092.51 2831.23 4923.74 -305.11 4 4 20190618 1667.40 832.29 974.92 617.24 1592.16 2499.69 ... ... ... ... ... ... ... ... ... 879 295 20190612 2032.73 912.14 1467.34 -181.33 1286.01 2944.87 880 296 20190611 2699.37 1038.56 3774.59 3171.37 6945.96 3737.93 881 297 20190610 1160.59 703.69 4957.98 2939.29 7897.27 1864.28 882 298 20190606 -13.56 -20.15 1500.24 -421.68 1078.56 -33.71 883 299 20190605 218.43 394.27 2276.22 781.60 3057.82 612.70 884 rows × 8 columns '''
'''
練習2
讀取 FoodFacts.csv 資料，該資料是全球食品資料，需分析每個國家新增劑的平均使用。
步驟分析
• 1.讀取資料
• 2.資料質量考量
• 3.清洗資料
• 4.對各個國家的使用數量進行統計
• 1.清洗,統計國家資料
• 2.通過國家統計新增劑用量
• 5.儲存統計結果
'''
data_food = pd.read_csv(r"FoodFacts.csv ")
data_food.info()
data_food.head(10)data = data_food.dropna(axis=1,how="all") data_food.info() data_food data_food.columns ''' Index(['code', 'url', 'creator', 'created_t', 'created_datetime', 'last_modified_t', 'last_modified_datetime', 'product_name', 'generic_name', 'quantity', ... 'caffeine_100g', 'taurine_100g', 'ph_100g', 'fruits_vegetables_nuts_100g', 'collagen_meat_protein_ratio_100g', 'cocoa_100g', 'chlorophyl_100g', 'carbon_footprint_100g', 'nutrition_score_fr_100g', 'nutrition_score_uk_100g'], dtype='object', length=159) ''' data1= pd.read_csv(r"FoodFacts.csv ",usecols=["countries_en","additives_n"]) data1.info() data1.head() ''' countries_en additives_n 0 France NaN 1 France NaN 2 France NaN 3 France NaN 4 France NaN ''' data1 = data1.dropna() data1 ''' countries_en additives_n 5 United Kingdom 0.0 6 France 0.0 8 France 0.0 10 United Kingdom 5.0 11 United Kingdom 5.0 ... ... ... 65480 United States 4.0 65490 France 0.0 65494 France 0.0 65499 France 0.0 65501 France 0.0 43616 rows × 2 columns ''' data_country = data1['countries_en'][~data1['countries_en'].str.contains(',')] count=data_country.drop_duplicates().count() total_countries = data_country.drop_duplicates() mean_additive_list=[] for country in total_countries: a = data1[data1["countries_en"].str.contains(country,case=False)] #print(a) mean_additive=a["additives_n"].mean() mean_additive_list.append(mean_additive) needed_data = pd.DataFrame({ "country":total_countries, "mean_additive":mean_additive_list }) needed_data ''' country mean_additive 5 United Kingdom 1.259009 6 France 1.930422 15 Spain 0.930324 22 Germany 0.777923 69 United States 2.180608 ... ... ... 62678 Iraq 1.500000 63052 Nederland 0.000000 64087 Singapore 1.000000 64096 Indonesia 2.125000 65403 Burkina Faso 1.666667 84 rows × 2 columns '''