# Code for loop that adds COUNTRY column for lab, row in cars.iterrows(): cars.loc[lab,'COUNTRY'] = str.upper(row['country']) # Print cars print(cars)
方法2:Iterrows循环 (速度等级::sheep:) i = 0 for ind, row in df.iterrows(): if row['test'] != 1: df1.iloc[i]['test'] = 0 i += 1 该循环方式是通过iterrows进行循环,ind和row分别代表了每一行的index和内容。测试例子大概需要0.07s,比起下标循环速度提升了321倍。 方法3:Apply循环(速度...
参考链接: 遍历Pandas DataFrame中的行和列 有如下 Pandas DataFrame: import pandas as pd inp = [{'c1':10, 'c2':100}, {...最佳解决方案 要以 Pandas 的方式迭代遍历DataFrame的行,可以使用: DataFrame.iterrows()for index, row in df.iterrows(): print...row["c1"], row["c2"] DataFrame.iter...
for index, r in t2info.iterrows(): calculation.append(r['number2']*t1info) maxrow = calculation.index(max(calculation)) return t2info.ix[maxrow] 相关讨论 apply没有矢量化。iterrows甚至更糟,因为它包装了所有东西(这与apply的性能不同)。您只应在极少数情况下使用iterrows。永远不要。显示您实际...
1、还是老生常谈的问题,不要使用iterrows(), itertuples(),尽量不要使用DataFrame.apply(),因为几个函数还是循环遍历的。 2、矢量化操作在字符串操作中也是可以使用的,但是为了安全起见,使用Numpy数组。 3、列表推导式就像它的名字一样,它还是一个list。
[88, 92, 95, 70]} # Convert the dictionary into DataFrame df = pd.DataFrame(data, columns = ['Name', 'Age', 'Stream', 'Percentage']) print("Given Dataframe :\n", df) print("\nIterating over rows using iterrows() method :\n") # iterate through each row and select # 'Name'...
方法2:Iterrows循环(速度等级: ) i = 0 for ind, row in df.iterrows(): if row['test'] != 1: df1.iloc[i]['test'] = 0 i += 1 该循环方式是通过iterrows进行循环,ind和row分别代表了每一行的index和内容。测试例子大概需要0.07s,比起下标循环速度提升了321倍。 方法3:Apply循环(速度等级: )...
mean ± std. dev. of 7 runs, 1 loop each)Iterrows方法比for循环更快，但itertuples方法是最快的。另外就是Apply方法允许我们对DF中的序列执行任何函数。def foo(val): if val > 50000: return "High" elif val <= 50000 and val > 10000: return "Mid Level" else: return "Lo...
#559 ms ± 10.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) #Read %timeit df=pd.read_csv("df.csv") #1.89 s ± 22.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) %timeit df=pd.read_pickle("df.pickle") ...
pandas dataframe loop 1. Use vectorized operations: Instead of using for loops, try to use vectorized operations like apply, map, or applymap, which can significantly improve the efficiency of your code. 2. Use iterrows() and itertuples() sparingly: These methods iterate over the rows of ...