#intdf["Age"].memory_usage(index=False, deep=False)#8000000#convertdf["Age"] = df["Age"].astype('int8')df["Age"].memory_usage(index=False, deep=False)#1000000#floatdf["Salary_After_Tax"] = df["Salary"] * 0.6df["Salary_After_Tax"].memory_usage(index=False, deep=False)#800...
mean_usage_b = selected_dtype.memory_usage(deep=True).mean() mean_usage_mb = mean_usage_b / 1024 ** 2 print("Average memory usage for {} columns: {:03.2f} MB".format(dtype,mean_usage_mb)) Average memory usage for float columns: 1.29 MB Average memory usage for int columns: 1.12...
mean_usage_b = selected_dtype.memory_usage(deep=True).mean() mean_usage_mb = mean_usage_b /1024**2 print("Average memory usage for {} columns: {:03.2f} MB".format(dtype,mean_usage_mb)) Average memory usageforfloat columns:1.29MB Average memory usageforint columns:1.12MB Average memor...
AI代码解释 triplets.info(memory_usage="deep")# Column Non-Null Count Dtype #---#0anchor525000non-nullcategory #1positive525000non-nullcategory #2negative525000non-nullcategory # dtypes:category(3)# memory usage:4.6MB# without categories triplets_raw.info(memory_usage="deep")# Column Non-Null ...
fordtypein['float','int','object']:selected_dtype=gl.select_dtypes(include=[dtype])mean_usage_b=selected_dtype.memory_usage(deep=True).mean()mean_usage_mb=mean_usage_b/1024**2print("Average memory usage for{}columns:{:03.2f}MB".format(dtype,mean_usage_mb))Averagememoryusageforfloatcolu...
>>>importpandasaspd>>>df=pd.read_csv("voters.csv")>>>df.info(verbose=False,memory_usage="deep")...memoryusage:71.2MB>>>df=df[["First Name","Last Name"]]>>>df.info(verbose=False,memory_usage="deep")...memoryusage:8.3MB
data.info()<class'pandas.core.frame.DataFrame'>RangeIndex:285entries,0to284Columns:1500entries,date to 2846Adtypes:float64(1497),int64(2),object(1)memory usage:3.3+MB 上述数据中包含285行,1500列,其中type列为object,date和hour列为int64类型,其余列均为float64类型。memory表明数据总共占用了约3.3M内...
memory usage: 165.9 MB 1.16 DataFrame.describe DataFrame.describe(percentiles=None, include=None, exclude=None, datetime_is_numeric=False) 生成描述性统计数据。 s = pd.Series([1, 2, 3]) >>> s.describe() count 3.0 mean 2.0 std 1.0 ...
# memoryusage:118.1MB 差异非常大,并且随着重复次数的增加,差异呈非线性增长。 2、行列转换 sql中经常会遇到行列转换的问题,Pandas有时候也需要,让我们看看来自Kaggle比赛的数据集。census_start .csv文件: 可以看到,这些按年来保存的,如果有一个列year和pct_bb,并且每一行有相应的值,则会好得多,对吧。
176字节，即每个字符串8个字节。但是，38个字符的字符串不能仅存储在8个字节中。df.memory_usage(...