import polars as pl import time # 读取 CSV 文件 start = time.time() df_pl_gpu = pl.read_csv('test_data.csv') load_time_pl_gpu = time.time() - start # 过滤操作 start = time.time() filtered_pl_gpu = df_pl_gpu.filter(pl.col('value1') > 50) filter_time_pl_gpu = time.t...
我们在get started目录中找how do I select a subset of a Dataframe->how do I filter specific rows from a dataframe(根据'select', 'filter', 'specific'这些关键词来看),我们得到的结果是,我们可以把它写成这样:delay_mean=dataframe[(dataframe["name"] == "endToEndDelay:mean")]。但是,我们还要“...
return list(filter(lambda item: item.visibility == 0, wb.sheets())) # 1、获取所有可看见的sheet sheet_visiable = self.get_all_visiable_sheets(wb) print('所有可见的sheet包含:', sheet_visiable) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 2、获取 Sheet 可见行或列 某一个 Sheet 中,...
Suppose, we are given a DataFrame with multiple columns and we need to drop those rows for which multiple columns have NaN values.Dropping row if two columns are NaNTo drop row if two columns are NaN, we will first create a DataFrame and then we will use the dropna() method inside ...
#for an object grouped on multiple columns: df.groupby(["A", "B"]).get_group(("bar", "one")) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 因此,在没有进行调用get_group(),也就是没有取出特定某一组数据之前,此时的数据结构任然是DataFrameGroupBy,其中也有很多函数和方法可以调用, ...
思路：将相同的数据中可以进行确认是相同的数据，拿来做分组的 key，这样保证不会重。 实际中使用，以...
有时候我们需要对ppt的模板进行复制,然后再添加相应内容,由于python-pptx对复制模板也没有很好的支持(我没找到~忧伤),所以我们用win32com对模板页进行复制,然后再用python-pptx增加ppt内容。 参考官方文档:https://docs.microsoft.com/zh-cn/office/vba/api/powerpoint.slide.copy ...
import numpy as npimport pandas as pdimport matplotlib.pyplot as pltfrom matplotlib import rcParamsimport seaborn as snsimport warningswarnings.filterwarnings('ignore') 在这里,我们将使用KNeighborsClassifier进行实验: from sklearn.neighbors import KNeighborsClassifier ...
clip columns combine combine_first compare 15. convert_dtypes copy corr corrwith count 16. cov cummax cummin cumprod cumsum 17. describe diff div divide dot 18. drop drop_duplicates droplevel dropna dtypes 19. duplicated empty eq equals eval 20. ewm expanding explode ffill fillna 21. filter ...
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