df.info(memory_usage='deep')<class'pandas.core.frame.DataFrame'>RangeIndex:307870entries,0to307869Datacolumns(total16columns):起点城市307870non-nullobject 起点城市代码307870non-nullint64 起点城市lng291690non-nullfloat64 起点城市lat291690non-nullfloat64 终点城市307870non-nullobject 终点城市代码307870non-...
Pandas是一个基于Python的数据分析工具库,可以用于数据清洗、数据处理、数据分析等任务。在Pandas中,可以使用一些方法来检查一列是否包含0,另一列是否为非null。 要检查一列是否包含0...
Year 5 non-null int64 Active 5 non-null object dtypes: float64(1), int64(3), object(6) memory usage: 440.0+ bytes 2. Numpy中的astype() astype()将第一列的数据转化为整数int类型。 #这样的操作并没有改变原始的数据框,而只是返回的一个拷贝df['Customer Number'].astype("int") ...
#1positive525000non-nullcategory #2negative525000non-nullcategory # dtypes: category(3) # memoryusage:4.6MB #withoutcategories triplets_raw.info(memory_usage="deep") #ColumnNon-NullCount Dtype #--- --- --- ---#0anchor525000non-nullobject#1positive525000non-nullobject#2negative525000non-nullobj...
55 non-null int64 8 Aggravated_assault 55 non-null int64 9 Burglary 55 non-null int64 10 Larceny_Theft 55 non-null int64 11 Vehicle_Theft 55 non-null int64 dtypes: datetime64[ns](1), int64(11)memory usage: 5.3 KB步骤6 将列Year设置为...
<class'pandas.core.frame.DataFrame'>RangeIndex: 458 entries, 0 to 457 # 行数,458 行,第一行编号为 0 Data columns (total 9 columns): # 列数,9列 # Column Non-Null Count Dtype # 各列的数据类型 --- --- --- --- 0 Name 457 non-null object 1 Team 457 non-null object 2 Number...
2 City 4 non-null object dtypes: int64(1), object(2) memory usage: 148.0+ bytes # 获取描述统计信息 Age count 4.000000 mean 32.500000 std 6.454972 min 25.000000 25% 27.500000 50% 32.500000 75% 37.500000 max 40.000000 # 按年龄排序 Name Age City ...
1 Customer Name 5 non-null object 2 2016 5 non-null object 3 2017 5 non-null object 4 Percent Growth 5 non-null object 5 Jan Units 5 non-null object 6 Month 5 non-null int64 7 Day 5 non-null int64 8 Year 5 non-null int64 9 Active 5 non-null object dtypes: float64(1), int...
1 int_col 4 non-null int64 2 float_col 4 non-null float64 3 mix_col 4 non-null object 4 missing_col 3 non-null float64 5 money_col 4 non-null object 6 boolean_col 4 non-null bool 7 custom 4 non-null object dtypes: bool(1), float64(2), int64(1), object(4) memory usage...
import pandas as pdnrows = 10000# 每次读取的行数df = pd.read_csv('large_file.csv', nrows=nrows):我们可以使用 info 函数来查看使用了多少内存。df.info()输出：<class 'pandas.core.frame.DataFrame'>RangeIndex:3 entries, to 2Data columns (total 2 columns):# Column Non-Null Count ...