pandas
数据处理库
读取文件
pandas.read_csv('xxxx.csv')
import pandas as pd m = pd.read_csv('m0.csv') print(type(m)) print(m.dtypes) # 字符值为object # print(help(pd.read_csv)) # <class 'pandas.core.frame.DataFrame'> # noteid int64 # notebook object # username object # date object # dtype: object
显示数据
m.head() #把数据显示出来，默认显示前五条 # m.head(3) #显示前3条 # m.tail(4) # 显示后四行 # print(m.columns) # 显示列的指标 # print(m.shape) # 查看数据维度（m,n）=》表示总共有m个样本,每个样本有n个指标
取数据
# location ### 取第一个数据 print(m.loc[0]) print('----------') print(m.loc[3]) ### 取从3开始到6结束 m.loc[3:6] ### 取1，3，5 id = {1,3,5} m.loc[id] ### 一列一列取 col = m['noteid'] # 列名来定义，如无则默认为第一行为列名 print(col) ### 取多列 col = ['noteid','username'] data = m[col] print(data) # 取以什么来结尾的列 # 比如找以(kg)结尾的 import pandas as pd m = pd.read_csv('m0.csv') col_names = m.columns.tolist() #把当前列名变成list print(col_names) gram_columns = [] for c in col_names: if c.endswith("(kg)"): # 以(kg)结尾 gram_columns.append(c) #追加 gram_def = m[gram_columns] print(gram_def.head(3)) # noteid 0 # school A # username adads # height(cm) 160 # weight(kg) 40 # date 2019/8/27 # Name: 0, dtype: object # ---------- # noteid 3 # school D # username zcvxvzxcv # height(cm) 163 # weight(kg) 43 # date 2019/8/30 # Name: 3, dtype: object
运算
# 四则运算 div_m = m['height(cm)'] / 100 print(div_m) # 0 1.60 # 1 1.61 # 2 1.62 # 3 1.63 # 4 1.64 # 5 1.65 # 6 1.66 # 7 1.67 # 8 1.68 # 9 1.69 # 10 1.70 # Name: height(cm), dtype: float64
新加一列
# 相同维度的运算 import pandas as pd m = pd.read_csv('m0.csv') data = m['height(cm)'] * m['weight(kg)'] print(data) print(data.shape) print('======新加一列======') height_m = m['height(cm)'] / 100 m['height(m)'] = height_m # 是添加了，但并未写入文件 print(m['height(m)']) print(m) # 0 6400 # 1 6601 # 2 6804 # 3 7009 # 4 7216 # 5 7425 # 6 7636 # 7 7849 # 8 8064 # 9 8281 # 10 8500 # dtype: int64 # (11,) # ======新加一列====== # 0 1.60 # 1 1.61 # 2 1.62 # 3 1.63 # 4 1.64 # 5 1.65 # 6 1.66 # 7 1.67 # 8 1.68 # 9 1.69 # 10 1.70 # Name: height(m), dtype: float64 # noteid school username height(cm) weight(kg) date height(m) # 0 0 A adads 160 40 2019/8/27 1.60 # 1 1 B sdfsadf 161 41 2019/8/28 1.61 # 2 2 C sdfasdf 162 42 2019/8/29 1.62 # 3 3 D zcvxvzxcv 163 43 2019/8/30 1.63 # 4 4 E sdfasf 164 44 2019/8/31 1.64 # 5 5 支持v在v在 必胜德国法国 165 45 2019/9/1 1.65 # 6 6 在v秩序册 在v出租车v 166 46 2019/9/2 1.66 # 7 7 支持v在v从 在v从中选出v 167 47 2019/9/3 1.67 # 8 8 v自行车v自行车v 在v出租车v 168 48 2019/9/4 1.68 # 9 9 在v现在v 自行车v在v从 169 49 2019/9/5 1.69 # 10 10 豆腐干豆腐干大锅饭 在v自行车v 170 50 2019/9/6 1.70
排序
import pandas as pd m = pd.read_csv('m0.csv') print('-------升序------') m.sort_values("weight(kg)",inplace=True) # 默认ascending为Ture，用来升序 print(m["weight(kg)"]) print('-------降序------') m.sort_values("weight(kg)",inplace=True,ascending=False) # 默认ascending为Ture，用来升序 print(m['weight(kg)']) # -------升序------ # 0 40 # 1 41 # 3 43 # 4 44 # 6 46 # 7 47 # 8 48 # 9 49 # 10 50 # 5 66 # 2 888 # Name: weight(kg), dtype: int64 # -------降序------ # 2 888 # 5 66 # 10 50 # 9 49 # 8 48 # 7 47 # 6 46 # 4 44 # 3 43 # 1 41 # 0 40 # Name: weight(kg), dtype: int64
NAN的含义
pandas认为NAN为缺失值，或打印不出来的值。一般把缺失值放到最后
math、numpy、pandas NaN 判断
>> np.nan == np.nan False >> np.nan is np.nan True >> math.nan is np.nan False >> np.isnan(math.nan) True
1. 判断 ndarray 中是否存在 nan
>> c = np.array([ 1., 2., np.nan, 3., 4.]) >> np.isnan(c) array([False, False, True, False, False])
- 注意
>> np.nan != np.nan True >> np.nan in c False
上述方案要么返回的是一个序列，要么给出的是错误的结果。判断 numpy 下的多维数组中是否存在 nan 的简单方式：
>> np.isnan(np.min(c)) True >> np.isnan(np.sum(c)) True
因为
>> np.min(c) nan >> np.sum(c) nan
2. 将 nan 填充为均值
>> c = np.array([ 1., 2., np.nan, 3., 4.]) >> c[np.isnan(c)] = np.mean(c[~np.nan(c)])
版权声明：本文为CSDN博主「Inside_Zhang」的原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接及本声明。原文链接：https://blog.csdn.net/lanchunhui/article/details/80399681
NAN例子
import pandas as pd import numpy as np data = pd.read_csv('titanic/train.csv') # https://www.kaggle.com/c/titanic/data data.head() # # 数据内容 # # pclass 仓位等级 # SibSp 兄弟姐妹数量 # Parch 父母/子女 # Fare 船票价格 # Cabin 床仓编号/NaN无该值 # Embarked 登船地点/码头 print('-------------') age = data["Age"] print(age.loc[0:10]) # 读取前十个值 print('@@@@@@@@@@@@@@@@@') age_is_null = pd.isnull(age) # 判断缺失值，false则不是，ture则是缺失值 print(age_is_null) print('@@@@@@@@@@@@@@@@') age_null_true = age[age_is_null] # 筛选，这里传入的为（true/false）,把true的留下来 print(age_null_true) print('@@@@@@@@@@@@@@@@') age_null_count = len(age_null_true) # 当前长度 print(age_null_count) # 未处理缺失值的情况 print('************未处理的情况**************') mean_age = sum(data['Age']) / len(data['Age']) # 有缺失值则结果为NaN, print(mean_age) # 处理后的情况 print('&&&&&&&&&&&&&&处理后的情况&&&&&&&&&&') good_ages = data['Age'][age_is_null == False] correct_mean_age = sum(good_ages) / len(good_ages) print(correct_mean_age) print('&&&&&&&&&&&&&&Pandas函数mean()来实现&&&&&&&&&&') # pandas默认的函数来实现以上功能 correct_mean_age = data['Age'].mean() print(correct_mean_age) # 每个仓位等级的平均价格 print('每个仓位等级的平均价格') passanger_classes = [1,2,3] fares_by_class = {} for this_class in passanger_classes: pclass_rows = data[data["Pclass"] == this_class ] pclass_fares = pclass_rows["Fare"] fare_for_class = pclass_fares.mean() fares_by_class[this_class] = fare_for_class print(fares_by_class) # pandas快速来实现 ## 函数pivot_table print('依靠函数pivot_table来实现上述功能') passanger_survival = data.pivot_table(index="Pclass",values="Fare",aggfunc=np.mean) # index:以谁为基准，values：index和什么的关系,aggfunc:指什么关系 print(passanger_survival) print('================================') # # 默认求平均值 # passanger_age = data.pivot_table(index="Pclass",values="Age") # 求平均年龄，少写一个aggfun。按照默认求均值来操作 print(passanger_age) print('++++++++++++++++++++++++++++++++') port_stats = data.pivot_table(index="Embarked",values=["Fare","Survived"],aggfunc=np.sum) #一个量和其它两个量之间的关系 print(port_stats) ## 函数 dropna print('==============dropna/把缺失值丢掉==================') #specifying axis = 1 or axis="columns" will drop any columns that have null values drop_na_columns = data.dropna(axis=1) print(drop_na_columns) print('++==++') new_passanger_survival = data.dropna(axis = 0,subset=['Age','Sex']) # 如果这俩列有缺失值，则把有缺失值当前对应的行给去掉 print(new_passanger_survival) print('^^^^^^^^^^^查找/定位到一个具体值^^^^^^^^^^^^^') row_index_83_age = data.loc[83,"Age"] # 83表示行，"Age"表示这一行"Age"这一列；一下同理 row_index_1000_pclass = data.loc[766,"Pclass"] print(row_index_83_age) print(row_index_1000_pclass) # ------------- # 0 22.0 # 1 38.0 # 2 26.0 # 3 35.0 # 4 35.0 # 5 NaN # 6 54.0 # 7 2.0 # 8 27.0 # 9 14.0 # 10 4.0 # Name: Age, dtype: float64 # @@@@@@@@@@@@@@@@@ # 0 False # 1 False # 2 False # 3 False # 4 False # ... # 886 False # 887 False # 888 True # 889 False # 890 False # Name: Age, Length: 891, dtype: bool # @@@@@@@@@@@@@@@@ # 5 NaN # 17 NaN # 19 NaN # 26 NaN # 28 NaN # .. # 859 NaN # 863 NaN # 868 NaN # 878 NaN # 888 NaN # Name: Age, Length: 177, dtype: float64 # @@@@@@@@@@@@@@@@ # 177 # ************未处理的情况************** # nan # &&&&&&&&&&&&&&处理后的情况&&&&&&&&&& # 29.69911764705882 # &&&&&&&&&&&&&&Pandas函数mean()来实现&&&&&&&&&& # 29.69911764705882 # 每个仓位等级的平均价格 # {1: 84.1546875, 2: 20.662183152173913, 3: 13.675550101832993} # 依靠函数pivot_table来实现上述功能 # Fare # Pclass # 1 84.154687 # 2 20.662183 # 3 13.675550 # ================================ # Age # Pclass # 1 38.233441 # 2 29.877630 # 3 25.140620 # ++++++++++++++++++++++++++++++++ # Fare Survived # Embarked # C 10072.2962 93 # Q 1022.2543 30 # S 17439.3988 217 # ==============dropna/把缺失值丢掉================== # PassengerId Survived Pclass \ # 0 1 0 3 # 1 2 1 1 # 2 3 1 3 # 3 4 1 1 # 4 5 0 3 # .. ... ... ... # 886 887 0 2 # 887 888 1 1 # 888 889 0 3 # 889 890 1 1 # 890 891 0 3 # Name Sex SibSp Parch \ # 0 Braund, Mr. Owen Harris male 1 0 # 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 1 0 # 2 Heikkinen, Miss. Laina female 0 0 # 3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 1 0 # 4 Allen, Mr. William Henry male 0 0 # .. ... ... ... ... # 886 Montvila, Rev. Juozas male 0 0 # 887 Graham, Miss. Margaret Edith female 0 0 # 888 Johnston, Miss. Catherine Helen "Carrie" female 1 2 # 889 Behr, Mr. Karl Howell male 0 0 # 890 Dooley, Mr. Patrick male 0 0 # Ticket Fare # 0 A/5 21171 7.2500 # 1 PC 17599 71.2833 # 2 STON/O2. 3101282 7.9250 # 3 113803 53.1000 # 4 373450 8.0500 # .. ... ... # 886 211536 13.0000 # 887 112053 30.0000 # 888 W./C. 6607 23.4500 # 889 111369 30.0000 # 890 370376 7.7500 # [891 rows x 9 columns] # ++==++ # PassengerId Survived Pclass \ # 0 1 0 3 # 1 2 1 1 # 2 3 1 3 # 3 4 1 1 # 4 5 0 3 # .. ... ... ... # 885 886 0 3 # 886 887 0 2 # 887 888 1 1 # 889 890 1 1 # 890 891 0 3 # Name Sex Age SibSp \ # 0 Braund, Mr. Owen Harris male 22.0 1 # 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 # 2 Heikkinen, Miss. Laina female 26.0 0 # 3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 # 4 Allen, Mr. William Henry male 35.0 0 # .. ... ... ... ... # 885 Rice, Mrs. William (Margaret Norton) female 39.0 0 # 886 Montvila, Rev. Juozas male 27.0 0 # 887 Graham, Miss. Margaret Edith female 19.0 0 # 889 Behr, Mr. Karl Howell male 26.0 0 # 890 Dooley, Mr. Patrick male 32.0 0 # Parch Ticket Fare Cabin Embarked # 0 0 A/5 21171 7.2500 NaN S # 1 0 PC 17599 71.2833 C85 C # 2 0 STON/O2. 3101282 7.9250 NaN S # 3 0 113803 53.1000 C123 S # 4 0 373450 8.0500 NaN S # .. ... ... ... ... ... # 885 5 382652 29.1250 NaN Q # 886 0 211536 13.0000 NaN S # 887 0 112053 30.0000 B42 S # 889 0 111369 30.0000 C148 C # 890 0 370376 7.7500 NaN Q # [714 rows x 12 columns] # ^^^^^^^^^^^查找^^^^^^^^^^^^^ # 28.0 # 1
排序
## 排序 import pandas as pd passanger_data = pd.read_csv('titanic/train.csv') new_passanger_survival = passanger_data.sort_values("Age",ascending=False) # 按年龄来降序排序 print(new_passanger_survival) passanger_reindex = new_passanger_survival.reset_index(drop=True) # 把index值（索引值）按降序规则来来重新排序 print('--------------------------') print(passanger_reindex.loc[0:10]) # \ 表示换行显示 # 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 # .. ... ... ... ... # 859 860 0 3 Razi, Mr. Raihed # 863 864 0 3 Sage, Miss. Dorothy Edith "Dolly" # 868 869 0 3 van Melkebeke, Mr. Philemon # 878 879 0 3 Laleff, Mr. Kristo # 888 889 0 3 Johnston, Miss. Catherine Helen "Carrie" # 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 # .. ... ... ... ... ... ... ... ... # 859 male NaN 0 0 2629 7.2292 NaN C # 863 female NaN 8 2 CA. 2343 69.5500 NaN S # 868 male NaN 0 0 345777 9.5000 NaN S # 878 male NaN 0 0 349217 7.8958 NaN S # 888 female NaN 1 2 W./C. 6607 23.4500 NaN S # [891 rows x 12 columns] # -------------------------- # 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 # 6 746 0 1 Crosby, Capt. Edward Gifford male # 7 34 0 2 Wheadon, Mr. Edward H male # 8 55 0 1 Ostby, Mr. Engelhart Cornelius male # 9 281 0 3 Duane, Mr. Frank male # 10 457 0 1 Millet, Mr. Francis Davis 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 # 6 70.0 1 1 WE/P 5735 71.0000 B22 S # 7 66.0 0 0 C.A. 24579 10.5000 NaN S # 8 65.0 0 1 113509 61.9792 B30 C # 9 65.0 0 0 336439 7.7500 NaN Q # 10 65.0 0 0 13509 26.5500 E38 S
自定义函数
import pandas as pd passanger_data = pd.read_csv('titanic/train.csv') # this function returns the bundbreath(第一百行) item from a series def hundredth_row(column): # Extract the hundredth item hundredth_items = column.loc[99] # start form 0 return hundredth_items # Return the hundredth item from each column hundredth_row = passanger_data.apply(hundredth_row) # apply 用来申请执行 print(hundredth_row) print() # 返回每一个缺失值的个数 def not_null_count(column): column_null = pd.isnull(column) null = column[column_null] return len(null) column_null_count = passanger_data.apply(not_null_count) print(column_null_count) # def which_class(row): pclass = row['Pclass'] if pd.isnull(pclass): return 'Unknown' elif pclass == 1: return 'First Class' elif pclass == 2: return 'Second Class' elif pclass == 3: return 'Third Class' classes = passanger_data.apply(which_class,axis = 1) # axis ?? means what?? print(classes) print() def is_minor(row): if row['Age'] < 18: return True else: return False minors = passanger_data.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 = passanger_data.apply(generate_age_label,axis = 1) print(age_labels) print('--------------') passanger_data['age_labels'] = age_labels age_group_survival = passanger_data.pivot_table(index="age_labels",values="Survived") #默认求各年龄获救的平局值 print(age_group_survival) # PassengerId 100 # Survived 0 # Pclass 2 # Name Kantor, Mr. Sinai # Sex male # Age 34 # SibSp 1 # Parch 0 # Ticket 244367 # Fare 26 # Cabin NaN # Embarked S # dtype: object # PassengerId 0 # Survived 0 # Pclass 0 # Name 0 # Sex 0 # Age 177 # SibSp 0 # Parch 0 # Ticket 0 # Fare 0 # Cabin 687 # Embarked 2 # dtype: int64 # 0 Third Class # 1 First Class # 2 Third Class # 3 First Class # 4 Third Class # ... # 886 Second Class # 887 First Class # 888 Third Class # 889 First Class # 890 Third Class # Length: 891, dtype: object # 0 adult # 1 adult # 2 adult # 3 adult # 4 adult # ... # 886 adult # 887 adult # 888 Unknown # 889 adult # 890 adult # Length: 891, dtype: object # -------------- # Survived # age_labels # Unknown 0.293785 # adult 0.381032 # minor 0.539823
Series
## Import the Series object from pandas ## ??? from pandas import Series passanger_data = pd.read_csv('titanic/train.csv') series_files = passanger_data['Name'] # 其中的一列 Passanger_name = series_files.values # series该列里面的值 # print(type(Passanger_name)) # print(Passanger_name) print('---------------') series_rt = passanger_data['Ticket'] rt_ticket = series_rt.values # print(rt_scores) series_custom = Series(rt_ticket,index = Passanger_name) # 用名字当索引 # series_custom[['Odahl, Mr. Nils Martin','Jonkoff, Mr. Lalio']] # 打印 这和下面大打印只能存在一个，如都存在，这一个不会显示 print('################') fiveten = series_custom[10:20] print(fiveten) ## 排序 print('\n\n--------排序-------------\n') series_files = passanger_data['PassengerId'] # 其中的一列 Passanger_id = series_files.values # series该列里面的值 series_age = passanger_data['Age'] Passanger_age = series_age.values series_custom1 = Series(Passanger_age,index = Passanger_id) # 把id当索引 original_index = series_custom1.index.tolist() #print original_index sorted_index = sorted(original_index) sorted_by_index = series_custom1.reindex(sorted_index) print(sorted_by_index) print('----按index（键）排序---------') sc2 = series_custom1.sort_index() # 按键排序 print(sc2) print('\n----按values(值)排序---------') # 按值排序 sc3 = series_custom1.sort_values() print(sc3) print('===========================') ## 数学运算 # The value in a Series object are treated as an ndayyat,the core data type in Numpy import numpy as np # Add each value with each other print(np.add(series_custom1,series_custom1)) # 值一样这之间相加，值不一样则对应相加???? # Apply sin function to each other # # np.sine(series_custom) # # module 'numpy' has no attribute 'sine' # Return the hightest value (vill return a single value not a Series) np.max(series_custom1) # will actually return a Series object with a boolean value for each ticket # > 50 返回一些true/false值 series_custom > 50 series_greater_than_50 = series_custom[series_custom > 50] # 拿true/false来返回值 series_one = series_custom > 50 series_two = series_custom < 75 both_criteria = series_custom[series_one & series_two] print(both_criteria) print('-=-=-=-=-=-=-=-=不同票价求平均值=-=-=-=-=-n\n\n\n\n\n') # data alignment same index # 不同票价求平均值 tt_critics = Series(passanger_data['ticket'].values,index=passanger_data['PassengerId']) tt_users = Series(passanger_data['ticket2'].values,index=passanger_data['PassengerId']) tt_mean= (tt_critics + tt_users) /2 print(tt_mean) # -------1-------- # #######2######## # Sandstrom, Miss. Marguerite Rut 10 # Bonnell, Miss. Elizabeth 11 # Saundercock, Mr. William Henry 12 # Andersson, Mr. Anders Johan 13 # Vestrom, Miss. Hulda Amanda Adolfina 14 # Hewlett, Mrs. (Mary D Kingcome) 15 # Rice, Master. Eugene 16 # Williams, Mr. Charles Eugene 17 # Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele) 18 # Masselmani, Mrs. Fatima 19 # dtype: int64 # --------排序------------- # 1 22.0 # 2 38.0 # 3 26.0 # 4 35.0 # 5 35.0 # ... # 887 27.0 # 888 19.0 # 889 NaN # 890 26.0 # 891 32.0 # Length: 891, dtype: float64 # ----按index（键）排序--------- # 1 22.0 # 2 38.0 # 3 26.0 # 4 35.0 # 5 35.0 # ... # 887 27.0 # 888 19.0 # 889 NaN # 890 26.0 # 891 32.0 # Length: 891, dtype: float64 # ----按values(值)排序--------- # 804 0.42 # 756 0.67 # 645 0.75 # 470 0.75 # 79 0.83 # ... # 860 NaN # 864 NaN # 869 NaN # 879 NaN # 889 NaN # Length: 891, dtype: float64 # =========================== # 1 44.0 # 2 76.0 # 3 52.0 # 4 70.0 # 5 70.0 # ... # 887 54.0 # 888 38.0 # 889 NaN # 890 52.0 # 891 64.0 # Length: 891, dtype: float64 # -=-=-=-=-=-=-=-=-=-=-=-=-=-n # Nosworthy, Mr. Richard Cater 51 # Harper, Mrs. Henry Sleeper (Myna Haxtun) 52 # Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson) 53 # Ostby, Mr. Engelhart Cornelius 54 # Woolner, Mr. Hugh 55 # Rugg, Miss. Emily 56 # Novel, Mr. Mansouer 57 # West, Miss. Constance Mirium 58 # Goodwin, Master. William Frederick 59 # Sirayanian, Mr. Orsen 60 # Icard, Miss. Amelie 61 # Harris, Mr. Henry Birkhardt 62 # Skoog, Master. Harald 63 # Stewart, Mr. Albert A 64 # Moubarek, Master. Gerios 65 # Nye, Mrs. (Elizabeth Ramell) 66 # Crease, Mr. Ernest James 67 # Andersson, Miss. Erna Alexandra 68 # Kink, Mr. Vincenz 69 # Jenkin, Mr. Stephen Curnow 70 # Goodwin, Miss. Lillian Amy 71 # Hood, Mr. Ambrose Jr 72 # Chronopoulos, Mr. Apostolos 73 # Bing, Mr. Lee 74 # dtype: int64 # -=-=-=-=-=-=-=-=-=-=-=-=-=-n # PassengerId # 1 5.0 # 2 5.5 # 3 6.0 # 4 6.5 # 5 7.0 # ... # 887 448.0 # 888 448.5 # 889 449.0 # 890 449.5 # 891 450.0 # Length: 891, dtype: float64
import pandas as pd # will return a new DataFrame that is indexed by the values in the specified column # and will drop that cloumn from the DataFrame # without the PannengerId dropped # DataFrame来指定一个索引值 passenger_data = pd.read_csv('titanic/train.csv') print(type(passanger_data) ) passenger_ticket = passanger_data.set_index('Name',drop=False) # 把ticket当成一个索引 print(passenger_ticket.index) # 打印index 值 # # 目前怀疑是数据的问题，一下索引都失败了 # 具体问题详 print('\n\n\n\n=========================') #Slice using either bracket notation or loc[] passenger_data["Moran,Mr.James":"Sandstrom,Miss.Marguerite Rut"] # Specific ticiket passanger_data.loc["Moran,Mr.James":"Sandstrom,Miss.Marguerite Rut"] # Select list of movies tickets = ["Sandstrom,Miss.Marguerite Rut","Moran,Mr.James","Rice,Master.Eugene"] passenger_data.loc[tickets] # <class 'pandas.core.frame.DataFrame'> # Index(['Braund, Mr. Owen Harris', # 'Cumings, Mrs. John Bradley (Florence Briggs Thayer)', # 'Heikkinen, Miss. Laina', # 'Futrelle, Mrs. Jacques Heath (Lily May Peel)', # 'Allen, Mr. William Henry', 'Moran, Mr. James', # 'McCarthy, Mr. Timothy J', 'Palsson, Master. Gosta Leonard', # 'Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)', # 'Nasser, Mrs. Nicholas (Adele Achem)', # ... # 'Markun, Mr. Johann', 'Dahlberg, Miss. Gerda Ulrika', # 'Banfield, Mr. Frederick James', 'Sutehall, Mr. Henry Jr', # 'Rice, Mrs. William (Margaret Norton)', 'Montvila, Rev. Juozas', # 'Graham, Miss. Margaret Edith', # 'Johnston, Miss. Catherine Helen "Carrie"', 'Behr, Mr. Karl Howell', # 'Dooley, Mr. Patrick'], # dtype='object', name='Name', length=891) # ========================= # --------------------------------------------------------------------------- # TypeError Traceback (most recent call last) # <ipython-input-31-6c844267ac99> in <module> # 18 print('\n\n\n\n=========================') # 19 #Slice using either bracket notation or loc[] # ---> 20 passenger_data["PassengerId":"ticket"] # 21 # 22 # Specific ticiket # F:\Software\PYTHON\lib\site-packages\pandas\core\frame.py in __getitem__(self, key) # 2959 # 2960 # Do we have a slicer (on rows)? # -> 2961 indexer = convert_to_index_sliceable(self, key) # 2962 if indexer is not None: # 2963 return self._slice(indexer, axis=0) # F:\Software\PYTHON\lib\site-packages\pandas\core\indexing.py in convert_to_index_sliceable(obj, key) # 2356 idx = obj.index # 2357 if isinstance(key, slice): # -> 2358 return idx._convert_slice_indexer(key, kind="getitem") # 2359 # 2360 elif isinstance(key, str): # F:\Software\PYTHON\lib\site-packages\pandas\core\indexes\base.py in _convert_slice_indexer(self, key, kind) # 3188 if self.is_integer() or is_index_slice: # 3189 return slice( # -> 3190 self._validate_indexer("slice", key.start, kind), # 3191 self._validate_indexer("slice", key.stop, kind), # 3192 self._validate_indexer("slice", key.step, kind), # F:\Software\PYTHON\lib\site-packages\pandas\core\indexes\base.py in _validate_indexer(self, form, key, kind) # 5069 pass # 5070 elif kind in ["iloc", "getitem"]: # -> 5071 self._invalid_indexer(form, key) # 5072 return key # 5073 # F:\Software\PYTHON\lib\site-packages\pandas\core\indexes\base.py in _invalid_indexer(self, form, key) # 3338 "cannot do {form} indexing on {klass} with these " # 3339 "indexers [{key}] of {kind}".format( # -> 3340 form=form, klass=type(self), key=key, kind=type(key) # 3341 ) # 3342 ) # TypeError: cannot do slice indexing on <class 'pandas.core.indexes.range.RangeIndex'> with these indexers [PassengerId] of <class 'str'>
类型转换
## 类型转换 # The apply() method in Pandas allows us to specify Python logic # The apply() method requires you to pass in a vectorized operation # that can be applied over each Series object. import numpy as np import pandas as pd passenger_data = pd.read_csv('titanic/train.csv') #returns the data types as a Series types = passenger_data.dtypes print(types) # filter data types to just floats,index attributes returns just column names float_columns = types[types.values == 'int64'].index #use bracket notation to filter columns to just float columns float_df = passenger_data[float_columns] print(float_df) # 'x' is a Series object representing a column deviations =float_df.apply(lambda x: np.std(x)) print('--------------------------') print(deviations) print('\n\n命名函数lambda') rt_mt_user = float_df[['ticket','ticket2']] rt_mt_user.apply(lambda x:np.std(x),axis=1) #对每一个指标算标准差 # PassengerId int64 # Survived int64 # Pclass int64 # Name object # Sex object # Age float64 # SibSp int64 # Parch int64 # Ticket object # Fare float64 # Cabin object # Embarked object # ticket int64 # ticket2 int64 # dtype: object # PassengerId Survived Pclass SibSp Parch ticket ticket2 # 0 1 0 3 1 0 0 10 # 1 2 1 1 1 0 1 10 # 2 3 1 3 0 0 2 10 # 3 4 1 1 1 0 3 10 # 4 5 0 3 0 0 4 10 # .. ... ... ... ... ... ... ... # 886 887 0 2 0 0 886 10 # 887 888 1 1 0 0 887 10 # 888 889 0 3 1 2 888 10 # 889 890 1 1 0 0 889 10 # 890 891 0 3 0 0 890 10 # [891 rows x 7 columns] # -------------------------- # PassengerId 257.209383 # Survived 0.486319 # Pclass 0.835602 # SibSp 1.102124 # Parch 0.805605 # ticket 257.209383 # ticket2 0.000000 # dtype: float64 # 命名函数lambda # 0 5.0 # 1 4.5 # 2 4.0 # 3 3.5 # 4 3.0 # ... # 886 438.0 # 887 438.5 # 888 439.0 # 889 439.5 # 890 440.0 # Length: 891, dtype: float64
人工智能/2.pandas.txt · 最后更改: 2019/10/05 15:47 由 emperinter
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