本文共 10498 字，大约阅读时间需要 34 分钟。

import pandas

读取csv数据：

food_info = pandas.read_csv("food_info.csv")print(type(food_info))

print(food_info.dtypes)#字符串为object结构

   
    NDB_No               int64Shrt_Desc           objectWater_(g)          float64Energ_Kcal           int64Protein_(g)        float64Lipid_Tot_(g)      float64Ash_(g)            float64Carbohydrt_(g)     float64Fiber_TD_(g)       float64Sugar_Tot_(g)      float64Calcium_(mg)       float64Iron_(mg)          float64Magnesium_(mg)     float64Phosphorus_(mg)    float64Potassium_(mg)     float64Sodium_(mg)        float64Zinc_(mg)          float64Copper_(mg)        float64Manganese_(mg)     float64Selenium_(mcg)     float64Vit_C_(mg)         float64Thiamin_(mg)       float64Riboflavin_(mg)    float64Niacin_(mg)        float64Vit_B6_(mg)        float64Vit_B12_(mcg)      float64Vit_A_IU           float64Vit_A_RAE          float64Vit_E_(mg)         float64Vit_D_mcg          float64Vit_D_IU           float64Vit_K_(mcg)        float64FA_Sat_(g)         float64FA_Mono_(g)        float64FA_Poly_(g)        float64Cholestrl_(mg)     float64dtype: object
   

显示数据内容：

food_info.head()

显示前n行数据：

food_info.head(n)显示后n行：food_info.tail(n)显示列名：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')

显示行列数：

food_info.shape

(8618, 36)

取数据：

按索引取数据： food_info.loc[0]

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

切片：

food_info.loc[3:6]

定位具体值：

food_info.loc[3,"NDB_No"]

按照列名： food_info["NDB_No"] 按照列名取多个数据： columns = ["Zinc_(mg)","Copper_(mg)"] food_info[columns]

取出以g结尾的数据：

col_names = food_info.columns.tolist()print(col_names)gram_columns = []for c in col_names:    if c.endswith("(g)"):        gram_columns.append(c)gram_df = food_info[gram_columns]print(gram_df.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)']   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

组合运算： result = food_info["Water_(g)"]*food_info["Energ_Kcal"]/1000 print(result.head(3))

0    11.378791    11.378792     0.21024dtype: float64

添加数据： food_info["Iron_(g)"] = result print(food_info.shape)

(8618, 37)

排序： food_info.sort_values("Sodium_(mg)",inplace=True,ascending=True)#ascending=True从小到大ascending=False从大到小;inplacenge=True不生成新数据 print(food_info["Sodium_(mg)"].head(100))

760     0.06470    0.0654     0.0655     0.02269    0.0610     0.0634     0.0637     0.0434     0.0673     0.0638     0.0632     0.0606     0.0633     0.06463    0.0630     0.0

判断数据是否非空： import pandas as pd import numpy as np titanic_survival = pd.read_csv("titanic_train.csv") age = titanic_survival["Age"] #print(age.loc[0:10]) age_is_null = pd.isnull(age)#NaN返回True # print (age_is_null) age_null_true = age[age_is_null] # print (age_null_true) age_null_count = len(age_null_true) print(age_null_count)

求均值：

correct_mean_age = titanic_survival["Age"].mean()print (correct_mean_age)

29.6h9911764705882

条件操作：

不同等级客舱获救平均人数：passenger_survival = titanic_survival.pivot_table(index="Pclass", values="Survived", aggfunc=np.mean)#index="Pclass"按照客舱等级，values="Survived"对获救人数进行操作， aggfunc=np.mean进行求平均print (passenger_survival)

SurvivedPclass          1       0.6296302       0.4728263       0.242363

不同上船地点的船票总费用和获救总人数： port_stats = titanic_survival.pivot_table(index="Embarked", values=["Fare","Survived"], aggfunc=np.sum) print(port_stats)

Fare  SurvivedEmbarked                      C         10072.2962        93Q          1022.2543        30S         17439.3988       217

丢弃缺失值： new_titanic_survival = titanic_survival.dropna(axis=0,subset=["Age", "Sex"])

重新排序：

new_titanic_survival = titanic_survival.sort_values("Age",ascending=False)print (new_titanic_survival[0:5])titanic_reindexed = new_titanic_survival.reset_index(drop=True)print('-------------')print(titanic_reindexed.loc[0:5])

PassengerId  Survived  Pclass                                  Name  \630          631         1       1  Barkworth, Mr. Algernon Henry Wilson   851          852         0       3                   Svensson, Mr. Johan   493          494         0       1               Artagaveytia, Mr. Ramon   96            97         0       1             Goldschmidt, Mr. George B   116          117         0       3                  Connors, Mr. Patrick         Sex   Age  SibSp  Parch    Ticket     Fare Cabin Embarked  630  male  80.0      0      0     27042  30.0000   A23        S  851  male  74.0      0      0    347060   7.7750   NaN        S  493  male  71.0      0      0  PC 17609  49.5042   NaN        C  96   male  71.0      0      0  PC 17754  34.6542    A5        C  116  male  70.5      0      0    370369   7.7500   NaN        Q  -------------   PassengerId  Survived  Pclass                                  Name   Sex  \0          631         1       1  Barkworth, Mr. Algernon Henry Wilson  male   1          852         0       3                   Svensson, Mr. Johan  male   2          494         0       1               Artagaveytia, Mr. Ramon  male   3           97         0       1             Goldschmidt, Mr. George B  male   4          117         0       3                  Connors, Mr. Patrick  male   5          673         0       2           Mitchell, Mr. Henry Michael  male       Age  SibSp  Parch      Ticket     Fare Cabin Embarked  0  80.0      0      0       27042  30.0000   A23        S  1  74.0      0      0      347060   7.7750   NaN        S  2  71.0      0      0    PC 17609  49.5042   NaN        C  3  71.0      0      0    PC 17754  34.6542    A5        C  4  70.5      0      0      370369   7.7500   NaN        Q  5  70.0      0      0  C.A. 24580  10.5000   NaN        S

使用自定义函数：

返回第一百行数据：

def hundredth_row(column):

    # Extract the hundredth item     hundredth_item = column.loc[99]     return hundredth_item hundredth_row = titanic_survival.apply(hundredth_row) print (hundredth_row)

PassengerId                  100Survived                       0Pclass                         2Name           Kantor, Mr. SinaiSex                         maleAge                           34SibSp                          1Parch                          0Ticket                    244367Fare                          26Cabin                        NaNEmbarked                       Sdtype: object

返回每一列的空数据个数： def not_null_count(column):     column_null = pd.isnull(column)     null = column[column_null]     return len(null) column_null_count = titanic_survival.apply(not_null_count) print (column_null_count)

PassengerId                  100Survived                       0Pclass                         2Name           Kantor, Mr. SinaiSex                         maleAge                           34SibSp                          1Parch                          0Ticket                    244367Fare                          26Cabin                        NaNEmbarked                       Sdtype: object

判断是否成年（离散化操作）：

def is_minor(row):    if row["Age"] < 18:        return True    else:

        return False

minors = titanic_survival.apply(is_minor, axis=1)

#print minors

def generate_age_label(row):     age = row["Age"]     if pd.isnull(age):         return "unknown"     elif age < 18:         return "minor"     else:         return "adult" age_labels = titanic_survival.apply(generate_age_label, axis=1) print (age_labels)

0        adult1        adult2        adult3        adult4        adult5      unknown6        adult7        minor8        adult9        minor10       minor11       adult12       adult13       adult14       minor15       adult16       minor17     unknown18       adult19     unknown20       adult21       adult22       minor23       adult24       minor25       adult

复合操作： titanic_survival['age_labels'] = age_labels age_group_survival = titanic_survival.pivot_table(index="age_labels", values="Survived") print (age_group_survival)

age_labelsadult      0.381032minor      0.539823unknown    0.293785Name: Survived, dtype: float64