在等于值的地方分配列 - pandas df

Question

我试图assign在pandas df. 具体来说，对于df下面的内容，我想用它Column['On']来确定当前发生了多少个值。然后我想将这些值以3. 所以值；1-3 = 14-6 = 27-9 = 3 etc这可以达到 20-30 个值。我考虑过 np.where 但它不是很有效而且我返回了一个错误。import pandas as pdimport numpy as npd = ({                    'On' : [1,2,3,4,5,6,7,7,6,5,4,3,2,1],                                           })df = pd.DataFrame(data=d)此调用有效：df['P'] = np.where(df['On'] == 1, df['On'],1)但是，如果我想将此应用于其他值，则会出现错误：df = df['P'] = np.where(df['On'] == 1, df['On'],1)df = df['P'] = np.where(df['On'] == 2, df['On'],1)df = df['P'] = np.where(df['On'] == 3, df['On'],1)IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices

Answer 1

你可以使用系列面具和 loc

df['P'] = float('nan')

df['P'].loc[(df['On'] >= 1) & (df['On'] <= 3)] = 1

df['P'].loc[(df['On'] >= 4) & (df['On'] <= 6)] = 2

# ...etc

用循环扩展它很容易

j = 1

for i in range(1, 20):

    df['P'].loc[(df['On'] >= j) & (df['On'] <= (j+2))] = i

    j += 3

Answer 2

通过一些基本的数学和矢量化，您可以获得更好的性能。

import pandas as pd

import numpy as np

n = 1000 

df = pd.DataFrame({"On":np.random.randint(1,20, n)})

AlexG的解决方案

%%time

j = 1

df["P"] =  np.nan

for i in range(1, 20):

    df['P'].loc[(df['On'] >= j) & (df['On'] <= (j+2))] = i

    j += 3

CPU times: user 2.11 s, sys: 0 ns, total: 2.11 s

Wall time: 2.11 s

建议的解决方案

%%time

df["P"] = np.ceil(df["On"]/3)

CPU times: user 2.48 ms, sys: 0 ns, total: 2.48 ms

Wall time: 2.15 ms

加速是 ~1000 倍