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Visualisation of missing-data occurrence frequency

2019-08-22 10:14发布

站内文章 / Python
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问题:

I'd like to create a 24x20 matrix(8 sections each has 60 cells or 6x10) for visualization of frequency of missing-data occurrence through cycles (=each 480-values) in dataset via panda dataframe and plot it for each columns 'A','B','C'.

So far I could map the create csv files and mapped the values in right way in matrix and plot it via sns.heatmap(df.isnull()) after changed the missing-data (nan & inf) into 0 or something like 0.01234 which has the least influence on data and in the other hand could be plotted. Below is my scripts so far:

import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib.pyplot as plt

def mkdf(ListOf480Numbers):
    normalMatrix = np.array_split(ListOf480Numbers,8)
    fixMatrix = []
    for i in range(8):
        lines = np.array_split(normalMatrix[i],6)
        newMatrix = [0,0,0,0,0,0]
        for j in (1,3,5):
            newMatrix[j] = lines[j]
        for j in (0,2,4):
            newMatrix[j] = lines[j][::-1]
        fixMatrix.append(newMatrix) 
    return fixMatrix

def print_df(fixMatrix):
    values = []
    for i in range(6):
        values.append([*fixMatrix[6][i], *fixMatrix[7][i]])
    for i in range(6):
        values.append([*fixMatrix[4][i], *fixMatrix[5][i]])
    for i in range(6):
        values.append([*fixMatrix[2][i], *fixMatrix[3][i]])
    for i in range(6):
        values.append([*fixMatrix[0][i], *fixMatrix[1][i]])
    df = pd.DataFrame(values)
    return (df)




dft = pd.read_csv('D:\Feryan.TXT', header=None)
id_set = dft[dft.index % 4 == 0].astype('int').values
A = dft[dft.index % 4 == 1].values
B = dft[dft.index % 4 == 2].values
C = dft[dft.index % 4 == 3].values
data = {'A': A[:,0], 'B': B[:,0], 'C': C[:,0]}

df = pd.DataFrame(data, columns=['A','B','C'], index = id_set[:,0])  

nan = np.array(df.isnull())
inf = np.array(df.isnull())
df = df.replace([np.inf, -np.inf], np.nan)
df[np.isinf(df)] = np.nan    # convert inf to nan
#dff = df[df.isnull().any(axis=1)]   # extract sub data frame

#df = df.fillna(0)
#df = df.replace(0,np.nan)



#next iteration create all plots, change the number of cycles
cycles = int(len(df)/480)
print(cycles)
for cycle in range(3):
    count =  '{:04}'.format(cycle)
    j = cycle * 480
    new_value1 = df['A'].iloc[j:j+480]
    new_value2 = df['B'].iloc[j:j+480]
    new_value3 = df['C'].iloc[j:j+480]
    df1 = print_df(mkdf(new_value1))
    df2 = print_df(mkdf(new_value2))
    df3 = print_df(mkdf(new_value3))              
    for i in df:
        try:
            os.mkdir(i)
        except:
            pass
            df1.to_csv(f'{i}/norm{i}{count}.csv', header=None, index=None) 
            df2.to_csv(f'{i}/norm{i}{count}.csv', header=None, index=None)
            df3.to_csv(f'{i}/norm{i}{count}.csv', header=None, index=None)

    #plotting all columns ['A','B','C'] in-one-window side by side


    fig, ax = plt.subplots(nrows=1, ncols=3 , figsize=(20,10))
    plt.subplot(131)

    ax = sns.heatmap(df1.isnull(), cbar=False)
    ax.axhline(y=6, color='w',linewidth=1.5)
    ax.axhline(y=12, color='w',linewidth=1.5)
    ax.axhline(y=18, color='w',linewidth=1.5)
    ax.axvline(x=10, color='w',linewidth=1.5)

    plt.title('Missing-data frequency in A', fontsize=20 , fontweight='bold', color='black', loc='center', style='italic')
    plt.axis('off')

    plt.subplot(132)
    ax = sns.heatmap(df2.isnull(), cbar=False)
    ax.axhline(y=6, color='w',linewidth=1.5)
    ax.axhline(y=12, color='w',linewidth=1.5)
    ax.axhline(y=18, color='w',linewidth=1.5)
    ax.axvline(x=10, color='w',linewidth=1.5)
    plt.title('Missing-data frequency in B', fontsize=20 , fontweight='bold', color='black', loc='center', style='italic')
    plt.axis('off')

    plt.subplot(133)
    ax = sns.heatmap(df3.isnull(), cbar=False)
    ax.axhline(y=6, color='w',linewidth=1.5)
    ax.axhline(y=12, color='w',linewidth=1.5)
    ax.axhline(y=18, color='w',linewidth=1.5)
    ax.axvline(x=10, color='w',linewidth=1.5) 
    plt.title('Missing-data frequency in C', fontsize=20 , fontweight='bold', color='black', loc='center', style='italic')
    plt.axis('off')

    plt.suptitle(f'Missing-data visualization', color='yellow', backgroundcolor='black', fontsize=15, fontweight='bold')
    plt.subplots_adjust(top=0.92, bottom=0.02, left=0.05, right=0.96, hspace=0.2, wspace=0.2)
    fig.text(0.035, 0.93, 'dataset1' , fontsize=19, fontweight='bold', rotation=42., ha='center', va='center',bbox=dict(boxstyle="round",ec=(1., 0.5, 0.5),fc=(1., 0.8, 0.8)))
    #fig.tight_layout()
    plt.savefig(f'{i}/result{count}.png') 
    #plt.show()

Problem is I don't know how could I plot frequency of missing-data occurrence correctly to understand in which sections and cells it happen frequently.

Note1 more missing value the color should be brighter and 100% missing data through cycles should be presented by white color and solid black color indicates non-missing-values. there could be a bar chart start from black color 0% to 100% white color.

Note2 I also provide sample text file of dataset for 3 cycles includes few missing data but it could be manually modified and increased : dataset

Expected result should be like below:

回答1:

You can store your nan/inf data in a seperate array that you can add up over the cycles for each nan/inf.

Your arrays always seem to have the same size, so I defined them with a fixed size. You can change that to match your data:

df1MissingDataFrequency = np.zeros((24,20))

Then you can add them up where you get a nan value (you have already replaced inf with nan in your code):

df1MissingDataFrequency = df1MissingDataFrequency + np.isnan(df1).astype(int)

over all your cycles.

You seem to have some problems with your indentation. I don't know if that is only the case for the code you posted here or if that is the same in your actual code, but at the moment you make a new plot each cycle and you redifine df1, df2, df3 for each i.

With your missing frequency data your code should look like this:

import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib.pyplot as plt

def mkdf(ListOf480Numbers):
    normalMatrix = np.array_split(ListOf480Numbers,8)
    fixMatrix = []
    for i in range(8):
        lines = np.array_split(normalMatrix[i],6)
        newMatrix = [0,0,0,0,0,0]
        for j in (1,3,5):
            newMatrix[j] = lines[j]
        for j in (0,2,4):
            newMatrix[j] = lines[j][::-1]
        fixMatrix.append(newMatrix) 
    return fixMatrix

def print_df(fixMatrix):
    values = []
    for i in range(6):
        values.append([*fixMatrix[6][i], *fixMatrix[7][i]])
    for i in range(6):
        values.append([*fixMatrix[4][i], *fixMatrix[5][i]])
    for i in range(6):
        values.append([*fixMatrix[2][i], *fixMatrix[3][i]])
    for i in range(6):
        values.append([*fixMatrix[0][i], *fixMatrix[1][i]])
    df = pd.DataFrame(values)
    return (df)


dft = pd.read_csv('D:/Feryan2.txt', header=None)
id_set = dft[dft.index % 4 == 0].astype('int').values
A = dft[dft.index % 4 == 1].values
B = dft[dft.index % 4 == 2].values
C = dft[dft.index % 4 == 3].values
data = {'A': A[:,0], 'B': B[:,0], 'C': C[:,0]}

df = pd.DataFrame(data, columns=['A','B','C'], index = id_set[:,0])  

nan = np.array(df.isnull())
inf = np.array(df.isnull())
df = df.replace([np.inf, -np.inf], np.nan)
df[np.isinf(df)] = np.nan    # convert inf to nan


df1MissingDataFrequency = np.zeros((24,20))
df2MissingDataFrequency = np.zeros((24,20))
df3MissingDataFrequency = np.zeros((24,20))


#next iteration create all plots, change the number of cycles
cycles = int(len(df)/480)
print(cycles)
for cycle in range(3):
    count =  '{:04}'.format(cycle)
    j = cycle * 480
    new_value1 = df['A'].iloc[j:j+480]
    new_value2 = df['B'].iloc[j:j+480]
    new_value3 = df['C'].iloc[j:j+480]
    df1 = print_df(mkdf(new_value1))
    df2 = print_df(mkdf(new_value2))
    df3 = print_df(mkdf(new_value3))              
    for i in df:
        try:
            os.mkdir(i)
        except:
            pass
    df1.to_csv(f'{i}/norm{i}{count}.csv', header=None, index=None) 
    df2.to_csv(f'{i}/norm{i}{count}.csv', header=None, index=None)
    df3.to_csv(f'{i}/norm{i}{count}.csv', header=None, index=None)

    df1MissingDataFrequency = df1MissingDataFrequency + np.isnan(df1).astype(int)
    df2MissingDataFrequency = df2MissingDataFrequency + np.isnan(df2).astype(int)
    df3MissingDataFrequency = df3MissingDataFrequency + np.isnan(df3).astype(int)

#plotting all columns ['A','B','C'] in-one-window side by side
fig, ax = plt.subplots(nrows=1, ncols=3 , figsize=(10,7))
plt.subplot(131)

ax = sns.heatmap(df1MissingDataFrequency, cbar=False, cmap="gray")
ax.axhline(y=6, color='w',linewidth=1.5)
ax.axhline(y=12, color='w',linewidth=1.5)
ax.axhline(y=18, color='w',linewidth=1.5)
ax.axvline(x=10, color='w',linewidth=1.5)

plt.title('Missing-data frequency in A', fontsize=20 , fontweight='bold', color='black', loc='center', style='italic')
plt.axis('off')

plt.subplot(132)
ax = sns.heatmap(df2MissingDataFrequency, cbar=False, cmap="gray")
ax.axhline(y=6, color='w',linewidth=1.5)
ax.axhline(y=12, color='w',linewidth=1.5)
ax.axhline(y=18, color='w',linewidth=1.5)
ax.axvline(x=10, color='w',linewidth=1.5)
plt.title('Missing-data frequency in B', fontsize=20 , fontweight='bold', color='black', loc='center', style='italic')
plt.axis('off')

plt.subplot(133)
ax = sns.heatmap(df3MissingDataFrequency, cbar=False, cmap="gray")
ax.axhline(y=6, color='w',linewidth=1.5)
ax.axhline(y=12, color='w',linewidth=1.5)
ax.axhline(y=18, color='w',linewidth=1.5)
ax.axvline(x=10, color='w',linewidth=1.5) 
plt.title('Missing-data frequency in C', fontsize=20 , fontweight='bold', color='black', loc='center', style='italic')
plt.axis('off')

plt.suptitle(f'Missing-data visualization', color='yellow', backgroundcolor='black', fontsize=15, fontweight='bold')
plt.subplots_adjust(top=0.92, bottom=0.02, left=0.05, right=0.96, hspace=0.2, wspace=0.2)
fig.text(0.035, 0.93, 'dataset1' , fontsize=19, fontweight='bold', rotation=42., ha='center', va='center',bbox=dict(boxstyle="round",ec=(1., 0.5, 0.5),fc=(1., 0.8, 0.8)))
#fig.tight_layout()
plt.savefig(f'{i}/result{count}.png') 
#plt.show()

Which gives you the output you want:

EDIT

In the spirit of DRY, I edited your code so you don't have df1, df2, df3, new_values1, ... and you copying and pasting the same things all over. You already loop over i, so you should use that to actually address the three different columns in your dataframe:

dft = pd.read_csv('C:/Users/frefra/Downloads/Feryan2.txt', header=None).replace([np.inf, -np.inf], np.nan)
id_set = dft[dft.index % 4 == 0].astype('int').values
A = dft[dft.index % 4 == 1].values
B = dft[dft.index % 4 == 2].values
C = dft[dft.index % 4 == 3].values
data = {'A': A[:,0], 'B': B[:,0], 'C': C[:,0]}
df = pd.DataFrame(data, columns=['A','B','C'], index = id_set[:,0])


new_values = []
dfs = []
nan_frequencies = np.zeros((3,24,20))

#next iteration create all plots, change the number of cycles
cycles = int(len(df)/480)
print(cycles)
for cycle in range(cycles):
    count =  '{:04}'.format(cycle)
    j = cycle * 480
    for idx,i in enumerate(df):
        try:
            os.mkdir(i)
        except:
            pass
        new_value = df[i].iloc[j:j+480]        
        new_values.append(new_value)
        dfi = print_df(mkdf(new_value))
        dfs.append(dfi)
        dfi.to_csv(f'{i}/norm{i}{count}.csv', header=None, index=None) 
        nan_frequencies[idx] = nan_frequencies[idx] + np.isnan(dfi).astype(int)


#plotting all columns ['A','B','C'] in-one-window side by side
fig, ax = plt.subplots(nrows=1, ncols=3 , figsize=(10,7))

for idx,i in enumerate(df):

    plt.subplot(1,3,idx+1)

    ax = sns.heatmap(nan_frequencies[idx], cbar=False, cmap="gray")
    ax.axhline(y=6, color='w',linewidth=1.5)
    ax.axhline(y=12, color='w',linewidth=1.5)
    ax.axhline(y=18, color='w',linewidth=1.5)
    ax.axvline(x=10, color='w',linewidth=1.5)

    plt.title('Missing-data frequency in ' + i, fontsize=20 , fontweight='bold', color='black', loc='center', style='italic')
    plt.axis('off')


标签： python seaborn missing-data find-occurrences cumulative-frequency
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