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...
non-null object 4 Age 332 non-null float64 5 SibSp 418 non-null int64 6 Parch 418 non-null int64 7 Ticket 418 non-null object 8 Fare 417 non-null float64 9 Cabin 91 non-null object 10 Embarked 418 non-null object dtypes: float64(2), int64(4), object(5) memory usage: 36.0+ ...
total 12 columns): # Column Non-Null Count Dtype --- --- --- --- 0 Year 55 non-null datetime64[ns] 1 Population 55 non-null int64 2 Total 55 non-null int64 3 Violent 55 non-null int64 4 ...
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-...
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") ...
<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...
<class'pandas.core.frame.DataFrame'>RangeIndex:5entries,0to4Datacolumns (total10columns):CustomerNumber5non-nullint64CustomerName5non-nullobject20165non-nullobject20175non-nullobjectPercentGrowth5non-nullobjectJanUnits5non-nullobjectMonth5non-nullint64Day5non-nullint64Year5non-nullint64Active5non-nullob...
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 ...
thresh: 表示删除空值的界限,传入一个整数。如果一行(或列)数据中少于thresh个非空值(non-NA values),则删除。也就是说,一行(或列)数据中至少要有thresh个非空值,否则删除。 subset: 删除空值时,只判断subset指定的列(或行)的子集,其他列(或行)中的空值忽略,不处理。当按行进行删除时,subset设置成列的子集...
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 ...