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Table in Matplotlib, can't get two columns?

Time:12-26

I'm struggling with tables for matplotlib (blume). The table is for an automation project that will produce 22 different maps. The code below produce a table with 49 rows. Some figures will only have 6 rows. When the number of rows exceeds 25 I would like to use two columns.

import pandas as pd
import matplotlib.pyplot as plt
from blume.table import table

# Dataframe

df=pd.DataFrame({'nr': [ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,
       35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49],
             'KnNamn': ['Härryda', 'Partille', 'Öckerö', 'Stenungsund', 'Tjörn', 'Orust',
       'Sotenäs', 'Munkedal', 'Tanum', 'Dals-Ed', 'Färgelanda', 'Ale',
       'Lerum', 'Vårgårda', 'Bollebygd', 'Grästorp', 'Essunga',
       'Karlsborg', 'Gullspång', 'Tranemo', 'Bengtsfors', 'Mellerud',
       'Lilla Edet', 'Mark', 'Svenljunga', 'Herrljunga', 'Vara', 'Götene',
       'Tibro', 'Töreboda', 'Göteborg', 'Mölndal', 'Kungälv', 'Lysekil',
       'Uddevalla', 'Strömstad', 'Vänersborg', 'Trollhättan', 'Alingsås',
       'Borås', 'Ulricehamn', 'Åmål', 'Mariestad', 'Lidköping', 'Skara',
       'Skövde', 'Hjo', 'Tidaholm', 'Falköping'],
             'rel': [0.03650425, 0.05022105, 0.03009109, 0.03966735, 0.02793296,
       0.03690838, 0.04757161, 0.05607283, 0.0546372 , 0.05452821,
       0.06640368, 0.04252673, 0.03677577, 0.05385784, 0.0407173 ,
       0.04024881, 0.05613226, 0.04476127, 0.08543165, 0.04070175,
       0.09281077, 0.08711656, 0.06111578, 0.04564958, 0.05058988,
       0.04618078, 0.04640402, 0.04826498, 0.08514253, 0.07799246,
       0.07829886, 0.04249149, 0.03909206, 0.06835601, 0.08027622,
       0.07087295, 0.09013876, 0.1040369 , 0.05004451, 0.06584845,
       0.04338739, 0.10570863, 0.0553109 , 0.05024871, 0.06531729,
       0.05565605, 0.05041816, 0.04885198, 0.07954831]})

# Table
fig,ax = plt.subplots(1, figsize=(10, 7))
    
val =[]

ax.axis('off')

for i, j, k in zip(df.nr, df.KnNamn, df.rel):
    k = k*100
    k = round(k,2)
    k= (str(k)   ' %')
    temp=str(i) '. '  str(j) ': '   str(k)
    val.append(temp)
    
val=[[el] for el in val]
#val=val[0]   val[1]

tab=table(ax,cellText=val, 
                #rowLabels=row_lab, 
                colLabels=['Relativ arbetslöshet'], loc='left', colWidths=[0.3], cellLoc='left')

plt.show()

As I understands it, if I want a table with two columns, my val object should be structured in a different way. In the case above, val is a nested list with 49 lists inside. I need to merge lists, I figure. I tried this pairwise for loop but that didn't work with range?

I'm sure there is a simple solution to this problem I have. Help would be much appreciated.

for i, j in zip(range(len(val)), range(len(val))[1:]   range(len(val))[:1]):
 print(i, j)
    

CodePudding user response:

I don't know if it is what you need but you could use zip() or better itertools.zip_longest() with val[:25], val[25:]

two_columns = []

for col1, col2 in itertools.zip_longest(values[:25], values[25:], fillvalue=''):
    #print(f'{col1:25} | {col2}')
    two_columns.append([col1, col2])

Full working example

import pandas as pd
import matplotlib.pyplot as plt
from blume.table import table
import itertools

df = pd.DataFrame({
    'nr': [
        1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
        18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,
        35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49
    ],
    'KnNamn': [
        'Härryda', 'Partille', 'Öckerö', 'Stenungsund', 'Tjörn', 'Orust',
        'Sotenäs', 'Munkedal', 'Tanum', 'Dals-Ed', 'Färgelanda', 'Ale',
        'Lerum', 'Vårgårda', 'Bollebygd', 'Grästorp', 'Essunga',
        'Karlsborg', 'Gullspång', 'Tranemo', 'Bengtsfors', 'Mellerud',
        'Lilla Edet', 'Mark', 'Svenljunga', 'Herrljunga', 'Vara', 'Götene',
        'Tibro', 'Töreboda', 'Göteborg', 'Mölndal', 'Kungälv', 'Lysekil',
        'Uddevalla', 'Strömstad', 'Vänersborg', 'Trollhättan', 'Alingsås',
        'Borås', 'Ulricehamn', 'Åmål', 'Mariestad', 'Lidköping', 'Skara',
        'Skövde', 'Hjo', 'Tidaholm', 'Falköping'
    ],
    'rel': [
        0.03650425, 0.05022105, 0.03009109, 0.03966735, 0.02793296,
        0.03690838, 0.04757161, 0.05607283, 0.0546372 , 0.05452821,
        0.06640368, 0.04252673, 0.03677577, 0.05385784, 0.0407173 ,
        0.04024881, 0.05613226, 0.04476127, 0.08543165, 0.04070175,
        0.09281077, 0.08711656, 0.06111578, 0.04564958, 0.05058988,
        0.04618078, 0.04640402, 0.04826498, 0.08514253, 0.07799246,
        0.07829886, 0.04249149, 0.03909206, 0.06835601, 0.08027622,
        0.07087295, 0.09013876, 0.1040369 , 0.05004451, 0.06584845,
        0.04338739, 0.10570863, 0.0553109 , 0.05024871, 0.06531729,
        0.05565605, 0.05041816, 0.04885198, 0.07954831
    ]
})

# df = df[:25]  # test for 25 rows

# ---

fig, ax = plt.subplots(1, figsize=(10, 7))
ax.axis('off')

# --- values ---
    
#values = []

#for number, name, rel in zip(df.nr, df.KnNamn, df.rel):
#    text = f'{number}. {name}: {rel*100:.2} %'
#    values.append(text)

values = df.apply(lambda row: f'{row["nr"]}. {row["KnNamn"]}: {row["rel"]*100:.2} %', axis=1).values

# --- columns ---

if len(values) > 25:
    two_columns = []
    
    for col1, col2 in itertools.zip_longest(values[:25], values[25:], fillvalue=''):
        #print(f'{col1:25} | {col2}')
        two_columns.append([col1, col2])

    tab = table(ax, cellText=two_columns, 
            #rowLabels=row_lab, 
            colLabels=['Col1', 'Col2'], colWidths=[0.3, 0.3], loc=-100, cellLoc='left')
else:
    one_column = [[item] for item in values]
    
    tab = table(ax, cellText=one_column, 
            #rowLabels=row_lab, 
            colLabels=['Col1'], colWidths=[0.3], loc=-100, cellLoc='left')
    
# --- plot ---    

plt.show()

Result:

enter image description here


EDIT:

More universal version which can create many columns.

Example automatically create 3 columns for ROWS = 20.

import pandas as pd
import matplotlib.pyplot as plt
from blume.table import table
import itertools

df = pd.DataFrame({
    'nr': [
        1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
        18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,
        35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49
    ],
    'KnNamn': [
        'Härryda', 'Partille', 'Öckerö', 'Stenungsund', 'Tjörn', 'Orust',
        'Sotenäs', 'Munkedal', 'Tanum', 'Dals-Ed', 'Färgelanda', 'Ale',
        'Lerum', 'Vårgårda', 'Bollebygd', 'Grästorp', 'Essunga',
        'Karlsborg', 'Gullspång', 'Tranemo', 'Bengtsfors', 'Mellerud',
        'Lilla Edet', 'Mark', 'Svenljunga', 'Herrljunga', 'Vara', 'Götene',
        'Tibro', 'Töreboda', 'Göteborg', 'Mölndal', 'Kungälv', 'Lysekil',
        'Uddevalla', 'Strömstad', 'Vänersborg', 'Trollhättan', 'Alingsås',
        'Borås', 'Ulricehamn', 'Åmål', 'Mariestad', 'Lidköping', 'Skara',
        'Skövde', 'Hjo', 'Tidaholm', 'Falköping'
    ],
    'rel': [
        0.03650425, 0.05022105, 0.03009109, 0.03966735, 0.02793296,
        0.03690838, 0.04757161, 0.05607283, 0.0546372 , 0.05452821,
        0.06640368, 0.04252673, 0.03677577, 0.05385784, 0.0407173 ,
        0.04024881, 0.05613226, 0.04476127, 0.08543165, 0.04070175,
        0.09281077, 0.08711656, 0.06111578, 0.04564958, 0.05058988,
        0.04618078, 0.04640402, 0.04826498, 0.08514253, 0.07799246,
        0.07829886, 0.04249149, 0.03909206, 0.06835601, 0.08027622,
        0.07087295, 0.09013876, 0.1040369 , 0.05004451, 0.06584845,
        0.04338739, 0.10570863, 0.0553109 , 0.05024871, 0.06531729,
        0.05565605, 0.05041816, 0.04885198, 0.07954831
    ]
})

#df = df[:25]  # test for 25 rows

# ---

fig, ax = plt.subplots(1, figsize=(10, 7))
ax.axis('off')

# --- values ---

def convert(row):
    return f'{row["nr"]}. {row["KnNamn"]}: {row["rel"]*100:.2} %'

values = df.apply(convert, axis=1).values

# --- columns ---

ROWS = 20
#ROWS = 25

columns = []
for idx in range(0, len(values), ROWS):
    columns.append(values[idx:idx ROWS])

columns_widths = [0.3] * len(columns)
columns_labels = [f'Col{i}' for i in range(1, len(columns) 1)]

rows = list(itertools.zip_longest(*columns, fillvalue=''))

# --- plot ---

tab = table(ax,
            cellText=rows, 
            #rowLabels=row_lab, 
            colLabels=columns_labels,
            colWidths=columns_widths,
            loc=-100,
            cellLoc='left')

plt.show()

Result:

enter image description here

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