dtypes) print("Data types on accessing a single column of the Data Frame ") print("Type of Names Column : ", type(table.iloc[:, 0])) print("Type of HouseNo Column : ", type(table.iloc[:, 3]), "\n") print("Data types of individual elements of a particular columns Data Frame...
下面是一个方便但很长的函数,它根据上表将浮点数和整数转换为它们的最小子类型: defreduce_memory_usage(df, verbose=True):numerics= ["int8","int16","int32","int64","float16","float32","float64"]start_mem = df.memory_usage.sum /1024**2forcol in df.columns:col_type = df[col].dtypes...
第一咱们来看看通过什么api来获取每一列的数据类型。 #grab all the columns data typeall_column_types = wine_reviews.dtypes 她的返回结果是一个series,如下所示 country object description object designation object points int64 price float64 province object region_1 object region_2 object taster_name obj...
data=pd.read_csv("shopping.csv") 3.1 数据集基础信息查询 data.shape# 行数列数data.dtypes# 所有列的数据类型data['id'].dtype# 某一列的数据类型data.ndim# 数据维度data.index# 行索引data.columns# 列索引data.values# 对象值 3.2 数据集整体情况查询 data.head() 4. 数据清洗 4.1 查看异常值 当...
<class'pandas.core.frame.DataFrame'>RangeIndex:5entries,0to4Datacolumns (total10columns):CustomerNumber5non-nullint64CustomerName5non-nullobject20165non-nullobject20175non-nullobjectPercentGrowth5non-nullobjectJanUnits5non-nullobjectMonth5non-nullint64Day5non-nullint64Year5non-nullint64Active5non-nullob...
Data columns (total 10 columns): # Column Non-Null Count Dtype --- --- --- --- 0 Customer Number 5 non-null float64 1 Customer Name 5 non-null object 2 2016 5 non-null object 3 2017 5 non-null object 4 Percent Growth 5 non-...
Data columns (total 10 columns): # Column Non-Null Count Dtype --- --- --- --- 0 Customer Number 5non-nullfloat64 1 Customer Name 5 non-null object 2 2016 5 non-null object 3 2017 5 non-null object 4 Percent Growth 5 non-...
Data columns (total 10 columns): # Column Non-Null Count Dtype --- --- --- --- 0 Customer Number 5 non-null float64 1 Customer Name 5 non-null object 2 2016 5 non-null object 3 2017 5 non-null object 4 Percent Growth 5 non-...
pandas.Dataframe(data,index,dtype,columns) 上述参数中,data可以为列表、array(数组)或dict(字典) 上述参数中,index表示行索引,columns代表列名或者列标签 一种表结构。 series和dataframe常用方法如下: list1=[['张三',23,'男'],['李四',27,'女'],['王二',26,'女']]#使用嵌套列表,每一行 ...
'data_types': df.dtypes.value_counts().to_dict(), 'unique_values': {col: df[col].nunique() for col in df.columns} } return pd.DataFrame(report.items(), columns=['Metric', 'Value']) 特征工程:# 创建新特征df['age_group'] = pd.cut(df['age'], bins=[0, 18, 35, 50, 100...