Gitter von Histogrammen nach gefilterten Daten

Question

diese Art von Datendatei vor:

Daten datei.txt

75,15,1,57.5,9.9,5 
75,15,1,58.1,10.0,5 
75,15,2,37.9,8.3,5 
75,15,2,18.2,7.3,5 
150,15,1,26.4,8.3,10 
150,15,1,31.6,7.9,10 
150,15,2,30.6,7.5,10 
150,15,2,25.1,7.1,10 

, dass Spaltenwerte nur 1,2 beachten 3. sind.

Ich möchte 3x2 -grid von Histogrammen produzieren. Die Subplots unten sehen richtig aus, aber jede Zeile sollte 2 Histogramme aus verschiedenen Datensätzen enthalten, ich meine, ich filtere die Daten gemäß der letzten Spalte.

Der wichtige Code ist ax.hist(X[ (y==grp) & (X[:,2]==1), cols],, in dem der Filter auftritt.

Ich mag 2 Histogramme in jeder Zeile:

• die 1. Reihe mit (X[:,2]== *) wo * jeder Wert aus der 3. Säule sein (1 oder 2),
• die zweite Reihe mit (X[:,2]==1) und
• der 3. Reihe mit (X[:,2]==2).

Im Lebenslauf erwarte ich, dass am 2. erhalten, 3. Reihen Histogramme für die gefilterten Daten:

3. Spalte Wert = 1

75,15,1,57.5,9.9,5 
75,15,1,58.1,10.0,5 
150,15,1,26.4,8.3,10 
150,15,1,31.6,7.9,10 

3. Spalte Wert = 2

75,15,2,37.9,8.3,5 
75,15,2,18.2,7.3,5 
150,15,2,30.6,7.5,10 
150,15,2,25.1,7.1,10 

Code:

#!/usr/bin/python 
# -*- coding: utf-8 -*- 

import pandas as pd 
import numpy as np 
import math 
from matplotlib import pyplot as plt 
from itertools import combinations 

data_file='data-file.txt' 

df = pd.io.parsers.read_csv(
    filepath_or_buffer=data_file, 
    delim_whitespace=False, 
    ) 

M, N = df.shape[0], df.shape[1] 

feature_dict = {i+1:label for i,label in zip(
       range(N), 
        ('L', 
        'A', 
        'G', 
        'P', 
        'T', 
        'PP', 
        ))} 

df.columns = [l for i,l in sorted(feature_dict.items())] 

X = df[range(N-1)].values 
y = df['PP'].values 

label_dict = dict(enumerate(sorted(list(set(y))))) 
label_dict = {x+1:y for x,y in label_dict.iteritems()} 
num_grupos = len(label_dict.keys()) 

grps_to_hist_list = [[j for j in i] for i in combinations(label_dict.keys(), 2)] 
grps_to_hist_list_values = [[j for j in i] for i in combinations(label_dict.values(), 2)] 

cols_to_hist = [3, 4] 

for grps_to_hist in grps_to_hist_list: 
    grps_str = [ label_dict[grps_to_hist[0]], label_dict[grps_to_hist[1]] ] 
    print 'creating histogram for groups %s from data file %s' % (grps_str , data_file) 
    fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(18,8)) 

    for ax,cols in zip(axes.ravel(), cols_to_hist): 
     # set bin sizes 
     min_b = math.floor(np.min(X[:,cols])) 
     max_b = math.ceil(np.max(X[:,cols])) 
     bins = np.linspace(min_b, max_b, 40) 


     # ploting the histograms 
     #""" 
     for grp,color in zip(grps_str, ('blue', 'red')): 
       ax.hist(X[ (y==grp) & (X[:,2]==1), cols], 
         color=color, 
         label='%s' % grp, 
         bins=bins, 
         alpha=0.3,) 

     ylims = ax.get_ylim() 

     # plot annotation 
     leg = ax.legend(loc='upper right', fancybox=True, fontsize=8) 
     leg.get_frame().set_alpha(0.5) 
     ax.set_ylim([0, max(ylims)+2]) 
     ax.set_xlabel(feature_dict[cols+1]) 
     ax.set_title('%s' % str(data_file)) 

     # hide axis ticks 
     ax.tick_params(axis="both", which="both", bottom="off", top="off", labelbottom="on", left="off", right="off", labelleft="on") 

     # remove axis spines 
     ax.spines["top"].set_visible(False) 
     ax.spines["right"].set_visible(False) 
     ax.spines["bottom"].set_visible(False) 
     ax.spines["left"].set_visible(False) 
     #""" 

    fig.tight_layout()  

    plt.show() 

Hier ist ein Screenshot aus dem obigen Code mit dem Filter (y==grp) & (X[:,2]==1) (die am 2. Reihe sein sollen).

Answer 1

Meine Logik ist über Zeilen mit entsprechenden Masken Ihrer Wahl zu wiederholen, [(X[:,2]==1) | (X[:,2]==2), X[:,2]==1, X[:,2]==2]. Hoffentlich ist das, was Sie wollen:

#!/usr/bin/python 
# -*- coding: utf-8 -*- 

import pandas as pd 
import numpy as np 
import math 
from matplotlib import pyplot as plt 
from itertools import combinations 

data_file='data-file.txt' 

df = pd.io.parsers.read_csv(
    filepath_or_buffer=data_file, 
    delim_whitespace=False, 
    ) 

M, N = df.shape[0], df.shape[1] 

feature_dict = {i+1:label for i,label in zip(
       range(N), 
        ('L', 
        'A', 
        'G', 
        'P', 
        'T', 
        'PP', 
        ))} 

df.columns = [l for i,l in sorted(feature_dict.items())] 

X = df[range(N-1)].values 
y = df['PP'].values 

label_dict = dict(enumerate(sorted(list(set(y))))) 
label_dict = {x+1:y for x,y in label_dict.iteritems()} 
num_grupos = len(label_dict.keys()) 

grps_to_hist_list = [[j for j in i] for i in combinations(label_dict.keys(), 2)] 
grps_to_hist_list_values = [[j for j in i] for i in combinations(label_dict.values(), 2)] 

cols_to_hist = [3, 4] 

for grps_to_hist in grps_to_hist_list: 
    grps_str = [ label_dict[grps_to_hist[0]], label_dict[grps_to_hist[1]] ] 
    print 'creating histogram for groups %s from data file %s' % (grps_str , data_file) 
    fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(18,8)) 

    for row_ax, row_mask in zip(axes, [(X[:,2]==1) | (X[:,2]==2), X[:,2]==1, X[:,2]==2]): 
     for ax,cols in zip(row_ax, cols_to_hist): 
      # set bin sizes 
      min_b = math.floor(np.min(X[:,cols])) 
      max_b = math.ceil(np.max(X[:,cols])) 
      bins = np.linspace(min_b, max_b, 40) 


      # ploting the histograms 
      #""" 
      for grp,color in zip(grps_str, ('blue', 'red')): 
        ax.hist(X[ (y==grp) & row_mask, cols], 
          color=color, 
          label='%s' % grp, 
          bins=bins, 
          alpha=0.3,) 

      ylims = ax.get_ylim() 

      # plot annotation 
      leg = ax.legend(loc='upper right', fancybox=True, fontsize=8) 
      leg.get_frame().set_alpha(0.5) 
      ax.set_ylim([0, max(ylims)+2]) 
      ax.set_xlabel(feature_dict[cols+1]) 
      ax.set_title('%s' % str(data_file)) 

      # hide axis ticks 
      ax.tick_params(axis="both", which="both", bottom="off", top="off", labelbottom="on", left="off", right="off", labelleft="on") 

      # remove axis spines 
      ax.spines["top"].set_visible(False) 
      ax.spines["right"].set_visible(False) 
      ax.spines["bottom"].set_visible(False) 
      ax.spines["left"].set_visible(False) 
      #""" 

    fig.tight_layout()  

    plt.show()