import pandas as pd import numpy as np df = pd.DataFrame( {"Quantity" :[4721,1647], "Total" : [236.05,82.35]}, index = [1,2]) df["CPS Gross"]= (df["Total"]/df["Quantity"]) conditions = [df["CPS Gross"] == 0.05] values = [0.03] df["CPS Calc"] = np.select(condition...
#To select rows whose column value is in an iterable array, which we'll define as array, you can use isin: array=['yellow','green'] df.loc[df['favorite_color'].isin(array)] 根据多列条件选择行: #Toselecta row based on multiple conditions you can use &: array= ['yellow','green'...
最终df如下所示。https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.loc.html
原文:pandas.pydata.org/docs/user_guide/pyarrow.html pandas 可以利用PyArrow来扩展功能并改善各种 API 的性能。这包括: 与NumPy 相比,拥有更广泛的数据类型 对所有数据类型支持缺失数据(NA) 高性能 IO 读取器集成 便于与基于 Apache Arrow 规范的其他数据框架库(例如 polars、cuDF)进行互操作性 要使用此...
# Using query for filtering rows with multiple conditions df.query('Order_Quantity > 3 and Customer_Fname == "Mary"') between():根据在指定范围内的值筛选行。df[df['column_name'].between(start, end)] # Filter rows based on values within a range ...
pandas 可以利用PyArrow来扩展功能并改善各种 API 的性能。这包括: 与NumPy 相比,拥有更广泛的数据类型 对所有数据类型支持缺失数据(NA) 高性能 IO 读取器集成 便于与基于 Apache Arrow 规范的其他数据框架库(例如 polars、cuDF)进行互操作性 要使用此功能,请确保您已经安装了最低支持的 PyArrow 版本。
# Using query for filtering rows with multiple conditionsdf.query('Order_Quantity >3and Customer_Fname =="Mary"') between():根据在指定范围内的值筛选行。df[df['column_name'].between(start, end)] #Filterrowsbasedonvalueswithina range ...
A step-by-step Python code example that shows how to select rows from a Pandas DataFrame based on a column's values. Provided by Data Interview Questions, a mailing list for coding and data interview problems.
#Usingqueryforfilteringrowswithmultiple conditions df.query('Order_Quantity > 3 and Customer_Fname == "Mary"') between():根据在指定范围内的值筛选行。df[df['column_name'].between(start, end)] #Filterrowsbasedonvalueswithina range df[df['Order Quantity'].between(3,5)] ...
基于多个条件的结果列pandas df使用numpy.select的经典方法：