Python 按行规范化熊猫数据帧
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Normalizing a pandas DataFrame by row
提问by ChrisB
What is the most idiomatic way to normalize each row of a pandas DataFrame? Normalizing the columns is easy, so one (very ugly!) option is:
规范化熊猫数据帧的每一行的最惯用的方法是什么？标准化列很容易，所以一个（非常丑陋！）选项是：
(df.T / df.T.sum()).T
Pandas broadcasting rules prevent df / df.sum(axis=1)from doing this
熊猫广播规则阻止df / df.sum(axis=1)这样做
采纳答案by joris
To overcome the broadcasting issue, you can use the divmethod:
要克服广播问题，您可以使用以下div方法：
df.div(df.sum(axis=1), axis=0)
See http://pandas.pydata.org/pandas-docs/stable/basics.html#matching-broadcasting-behavior
见http://pandas.pydata.org/pandas-docs/stable/basics.html#matching-broadcasting-behavior
回答by Rafa
I would suggest to use Scikit preprocessinglibraries and transpose your dataframe as required:
我建议使用Scikit 预处理库并根据需要转置数据帧：
'''
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()
