按行规范化 pandas DataFrame

Posted 2023-03-12

【中文标题】按行规范化 pandas DataFrame

【英文标题】：Normalizing a pandas DataFrame by row

【发布时间】：2013-09-06 19:36:40

【问题描述】：

规范化 pandas DataFrame 的每一行的最惯用的方法是什么？规范化列很容易，所以一个（非常难看！）选项是：

(df.T / df.T.sum()).T

Pandas 广播规则阻止 df / df.sum(axis=1) 这样做

【参考方案1】：

要解决广播问题，您可以使用div 方法：

df.div(df.sum(axis=1), axis=0)

见pandas User Guide: Matching / broadcasting behavior

【参考方案2】：

我建议使用Scikit preprocessing 库并根据需要转置您的数据框：

'''
Created on 05/11/2015

@author: rafaelcastillo
'''

import matplotlib.pyplot as plt
import pandas
import random
import numpy as np
from sklearn import preprocessing

def create_cos(number_graphs,length,amp):
    # This function is used to generate cos-kind graphs for testing
    # number_graphs: to plot
    # length: number of points included in the x axis
    # amp: Y domain modifications to draw different shapes
    x = np.arange(length)
    amp = np.pi*amp
    xx = np.linspace(np.pi*0.3*amp, -np.pi*0.3*amp, length)
    for i in range(number_graphs):
        iterable = (2*np.cos(x) + random.random()*0.1 for x in xx)
        y = np.fromiter(iterable, np.float)
        if i == 0: 
            yfinal =  y
            continue
        yfinal = np.vstack((yfinal,y))
    return x,yfinal

x,y = create_cos(70,24,3)
data = pandas.DataFrame(y)

x_values = data.columns.values
num_rows = data.shape[0]

fig, ax = plt.subplots()
for i in range(num_rows):
    ax.plot(x_values, data.iloc[i])
ax.set_title('Raw data')
plt.show() 

std_scale = preprocessing.MinMaxScaler().fit(data.transpose())
df_std = std_scale.transform(data.transpose())
data = pandas.DataFrame(np.transpose(df_std))


fig, ax = plt.subplots()
for i in range(num_rows):
    ax.plot(x_values, data.iloc[i])
ax.set_title('Data Normalized')
plt.show()                                   

【讨论】：

除了涉及preprocessing.MinMaxScaler 和相应import 的三行之外，所有这些绘图代码都无关紧要。你能减少你的答案吗？