Visualization of multiple columns

Question

I have the following dataset :

{'PRODUCTLINE': {0: 'Classic Cars',
  1: 'Classic Cars',
  2: 'Classic Cars',
  3: 'Classic Cars',
  4: 'Classic Cars',
  5: 'Classic Cars',
  6: 'Classic Cars',
  7: 'Classic Cars',
  8: 'Classic Cars',
  9: 'Classic Cars',
  10: 'Classic Cars',
  11: 'Classic Cars',
  12: 'Classic Cars',
  13: 'Classic Cars',
  14: 'Classic Cars',
  15: 'Classic Cars',
  16: 'Classic Cars',
  17: 'Classic Cars',
  18: 'Classic Cars',
  19: 'Motorcycles',
  20: 'Motorcycles',
  21: 'Motorcycles',
  22: 'Motorcycles',
  23: 'Motorcycles',
  24: 'Motorcycles',
  25: 'Motorcycles',
  26: 'Motorcycles',
  27: 'Motorcycles',
  28: 'Motorcycles',
  29: 'Motorcycles',
  30: 'Motorcycles',
  31: 'Motorcycles',
  32: 'Motorcycles',
  33: 'Motorcycles',
  34: 'Motorcycles',
  35: 'Planes',
  36: 'Planes',
  37: 'Planes',
  38: 'Planes',
  39: 'Planes',
  40: 'Planes',
  41: 'Planes',
  42: 'Planes',
  43: 'Planes',
  44: 'Planes',
  45: 'Planes',
  46: 'Planes',
  47: 'Planes',
  48: 'Planes',
  49: 'Planes',
  50: 'Planes',
  51: 'Planes',
  52: 'Ships',
  53: 'Ships',
  54: 'Ships',
  55: 'Ships',
  56: 'Ships',
  57: 'Ships',
  58: 'Ships',
  59: 'Ships',
  60: 'Ships',
  61: 'Ships',
  62: 'Ships',
  63: 'Ships',
  64: 'Ships',
  65: 'Ships',
  66: 'Ships',
  67: 'Trains',
  68: 'Trains',
  69: 'Trains',
  70: 'Trains',
  71: 'Trains',
  72: 'Trains',
  73: 'Trains',
  74: 'Trains',
  75: 'Trains',
  76: 'Trains',
  77: 'Trains',
  78: 'Trains',
  79: 'Trains',
  80: 'Trains',
  81: 'Trains',
  82: 'Trucks and Buses',
  83: 'Trucks and Buses',
  84: 'Trucks and Buses',
  85: 'Trucks and Buses',
  86: 'Trucks and Buses',
  87: 'Trucks and Buses',
  88: 'Trucks and Buses',
  89: 'Trucks and Buses',
  90: 'Trucks and Buses',
  91: 'Trucks and Buses',
  92: 'Trucks and Buses',
  93: 'Trucks and Buses',
  94: 'Trucks and Buses',
  95: 'Trucks and Buses',
  96: 'Trucks and Buses',
  97: 'Trucks and Buses',
  98: 'Vintage Cars',
  99: 'Vintage Cars',
  100: 'Vintage Cars',
  101: 'Vintage Cars',
  102: 'Vintage Cars',
  103: 'Vintage Cars',
  104: 'Vintage Cars',
  105: 'Vintage Cars',
  106: 'Vintage Cars',
  107: 'Vintage Cars',
  108: 'Vintage Cars',
  109: 'Vintage Cars',
  110: 'Vintage Cars',
  111: 'Vintage Cars',
  112: 'Vintage Cars',
  113: 'Vintage Cars',
  114: 'Vintage Cars',
  115: 'Vintage Cars'},
 'COUNTRY': {0: 'Australia',
  1: 'Austria',
  2: 'Belgium',
  3: 'Canada',
  4: 'Denmark',
  5: 'Finland',
  6: 'France',
  7: 'Germany',
  8: 'Ireland',
  9: 'Italy',
  10: 'Japan',
  11: 'Norway',
  12: 'Philippines',
  13: 'Singapore',
  14: 'Spain',
  15: 'Sweden',
  16: 'Switzerland',
  17: 'UK',
  18: 'USA',
  19: 'Australia',
  20: 'Austria',
  21: 'Canada',
  22: 'Finland',
  23: 'France',
  24: 'Germany',
  25: 'Ireland',
  26: 'Italy',
  27: 'Japan',
  28: 'Norway',
  29: 'Philippines',
  30: 'Singapore',
  31: 'Spain',
  32: 'Sweden',
  33: 'UK',
  34: 'USA',
  35: 'Australia',
  36: 'Austria',
  37: 'Belgium',
  38: 'Canada',
  39: 'Denmark',
  40: 'Finland',
  41: 'France',
  42: 'Germany',
  43: 'Ireland',
  44: 'Italy',
  45: 'Japan',
  46: 'Norway',
  47: 'Philippines',
  48: 'Spain',
  49: 'Sweden',
  50: 'UK',
  51: 'USA',
  52: 'Australia',
  53: 'Austria',
  54: 'Belgium',
  55: 'Canada',
  56: 'Denmark',
  57: 'Finland',
  58: 'France',
  59: 'Germany',
  60: 'Italy',
  61: 'Japan',
  62: 'Singapore',
  63: 'Spain',
  64: 'Sweden',
  65: 'UK',
  66: 'USA',
  67: 'Australia',
  68: 'Belgium',
  69: 'Denmark',
  70: 'Finland',
  71: 'France',
  72: 'Germany',
  73: 'Ireland',
  74: 'Italy',
  75: 'Japan',
  76: 'Norway',
  77: 'Singapore',
  78: 'Spain',
  79: 'Sweden',
  80: 'UK',
  81: 'USA',
  82: 'Australia',
  83: 'Austria',
  84: 'Canada',
  85: 'Denmark',
  86: 'Finland',
  87: 'France',
  88: 'Germany',
  89: 'Ireland',
  90: 'Italy',
  91: 'Japan',
  92: 'Norway',
  93: 'Singapore',
  94: 'Spain',
  95: 'Sweden',
  96: 'UK',
  97: 'USA',
  98: 'Australia',
  99: 'Austria',
  100: 'Belgium',
  101: 'Canada',
  102: 'Denmark',
  103: 'Finland',
  104: 'France',
  105: 'Germany',
  106: 'Ireland',
  107: 'Italy',
  108: 'Japan',
  109: 'Norway',
  110: 'Philippines',
  111: 'Singapore',
  112: 'Spain',
  113: 'Sweden',
  114: 'UK',
  115: 'USA'},
 'QUANTITYORDERED': {0: 1818,
  1: 937,
  2: 147,
  3: 456,
  4: 1244,
  5: 1284,
  6: 3540,
  7: 1281,
  8: 202,
  9: 948,
  10: 314,
  11: 1158,
  12: 478,
  13: 1043,
  14: 4380,
  15: 552,
  16: 1078,
  17: 1507,
  18: 11625,
  19: 876,
  20: 197,
  21: 41,
  22: 447,
  23: 2404,
  24: 121,
  25: 58,
  26: 77,
  27: 309,
  28: 484,
  29: 241,
  30: 44,
  31: 780,
  32: 133,
  33: 371,
  34: 5080,
  35: 813,
  36: 200,
  37: 41,
  38: 317,
  39: 70,
  40: 421,
  41: 1136,
  42: 245,
  43: 115,
  44: 1122,
  45: 547,
  46: 325,
  47: 215,
  48: 1101,
  49: 104,
  50: 479,
  51: 3476,
  52: 56,
  53: 113,
  54: 343,
  55: 486,
  56: 436,
  57: 315,
  58: 766,
  59: 55,
  60: 194,
  61: 208,
  62: 174,
  63: 1388,
  64: 367,
  65: 831,
  66: 2395,
  67: 33,
  68: 97,
  69: 134,
  70: 89,
  71: 222,
  72: 89,
  73: 50,
  74: 82,
  75: 49,
  76: 72,
  77: 174,
  78: 509,
  79: 32,
  80: 168,
  81: 912,
  82: 705,
  83: 203,
  84: 517,
  85: 73,
  86: 384,
  87: 1067,
  88: 81,
  89: 37,
  90: 47,
  91: 102,
  92: 308,
  93: 888,
  94: 1709,
  95: 433,
  96: 291,
  97: 3932,
  98: 1945,
  99: 324,
  100: 446,
  101: 476,
  102: 240,
  103: 252,
  104: 1955,
  105: 276,
  106: 28,
  107: 1303,
  108: 313,
  109: 495,
  110: 27,
  111: 437,
  112: 2562,
  113: 385,
  114: 1366,
  115: 8239},
 'SALES': {0: 193085.5400000001,
  1: 101459.47,
  2: 20136.960000000003,
  3: 61623.219999999994,
  4: 157182.48000000004,
  5: 153552.24000000002,
  6: 388951.2000000002,
  7: 148314.99999999997,
  8: 31688.82,
  9: 128576.65,
  10: 47271.49,
  11: 134787.36999999997,
  12: 53112.090000000004,
  13: 132890.44,
  14: 476165.1499999998,
  15: 69088.06000000001,
  16: 117713.55999999998,
  17: 159377.69999999998,
  18: 1344638.2199999993,
  19: 89968.76,
  20: 26047.66,
  21: 4177.49,
  22: 47866.72,
  23: 226390.30999999997,
  24: 7497.500000000001,
  25: 4953.200000000001,
  26: 7567.8,
  27: 26536.41,
  28: 51768.63,
  29: 18061.68,
  30: 4175.6,
  31: 74634.82000000002,
  32: 15567.25,
  33: 40802.810000000005,
  34: 520371.70000000024,
  35: 74853.87000000001,
  36: 17860.44,
  37: 5624.79,
  38: 25510.07,
  39: 7586.45,
  40: 34375.130000000005,
  41: 108155.51000000002,
  42: 23001.26,
  43: 11784.36,
  44: 98185.65000000001,
  45: 49176.96000000001,
  46: 29500.7,
  47: 20906.87,
  48: 89985.51,
  49: 8899.6,
  50: 41163.51,
  51: 328432.88999999996,
  52: 4159.76,
  53: 9024.73,
  54: 31708.010000000002,
  55: 40309.01,
  56: 38697.259999999995,
  57: 29808.440000000002,
  58: 66486.67,
  59: 5501.0,
  60: 17703.54,
  61: 18860.02,
  62: 14155.519999999999,
  63: 124459.96999999997,
  64: 30915.89,
  65: 72959.17000000001,
  66: 209688.13999999998,
  67: 1681.35,
  68: 9017.26,
  69: 11476.330000000002,
  70: 5117.05,
  71: 27340.8,
  72: 5043.42,
  73: 3112.6,
  74: 6274.959999999999,
  75: 3523.67,
  76: 11310.36,
  77: 13278.71,
  78: 43370.17999999999,
  79: 3807.68,
  80: 12635.539999999999,
  81: 69253.56,
  82: 77318.49999999999,
  83: 20472.75,
  84: 51945.98,
  85: 9588.82,
  86: 40479.329999999994,
  87: 116982.22000000003,
  88: 10178.0,
  89: 3983.05,
  90: 5914.969999999999,
  91: 13349.44,
  92: 37075.64,
  93: 89027.68000000002,
  94: 177556.78000000003,
  95: 47931.270000000004,
  96: 28142.989999999998,
  97: 397842.4200000002,
  98: 189555.32000000004,
  99: 27197.480000000003,
  100: 41925.6,
  101: 40512.79,
  102: 21105.81,
  103: 18383.0,
  104: 176609.81,
  105: 20935.91,
  106: 2234.4,
  107: 110450.74000000003,
  108: 29449.82,
  109: 43021.0,
  110: 1935.09,
  111: 34960.46,
  112: 229514.51,
  113: 33804.45999999999,
  114: 123798.73999999999,
  115: 757755.9}}

I want to visualize the frequency of each category ordered in a country.

For example, in USA we ordered "X" amount of Products and "y" of them were Classic Cars and so on. So far, I only reached that it shows the overall frequency of each productline. How can I integrate the country?

sales_by_categorie = df_clean.groupby(['PRODUCTLINE', "COUNTRY"])[['QUANTITYORDERED', 'SALES']].sum().reset_index()

bar_data1 = go.Bar(
      x = sales_by_categorie['PRODUCTLINE'],
      y = sales_by_categorie['QUANTITYORDERED'],
      name = 'Quantity Ordered',
      text = sales_by_categorie['QUANTITYORDERED'],
      texttemplate = '%{text:.2s}',
      textposition = 'inside',
      yaxis = 'y1',
      offsetgroup=1,
      )

figure2 = go.Figure(bar_data1 )

figure2.show()

Hello! In order to receive help, it's best for you to make your question replicable: do so by including a dataset copyable by a respondent. — preritdas
– preritdas, Commented Mar 14, 2022 at 16:24
It's best practice to print your data as a dictionary using pd.to_dict() and copy that data to Stack. — preritdas
– preritdas, Commented Mar 14, 2022 at 16:29
What is your expected output? How is what you have different from what you want? — not_speshal
– not_speshal, Commented Mar 14, 2022 at 16:40

not_speshal · Accepted Answer · 2022-03-14 16:56:51Z

2

IIUC, try:

import matplotlib.pylot as plt

output = df_clean.pivot_table("QUANTITYORDERED","PRODUCTLINE","COUNTRY","sum")
f = plt.figure()
output.plot.bar(stacked=True, ax=f.gca())
plt.legend(loc="center left", bbox_to_anchor=(1, 0.5))

Stacked bar chart

If you instead want country on the X-axis and the breakdown of products on the Y-axis, you can do:

f = plt.figure()
output.T.plot.bar(stacked=True, ax=f.gca())
plt.legend(loc="center left", bbox_to_anchor=(1, 0.5))

enter image description here

answered Mar 14, 2022 at 16:56

not_speshal

23.2k2 gold badges18 silver badges33 bronze badges

Sign up to request clarification or add additional context in comments.

2 Comments

Nine x Seven Prod. Over a year ago

thanks! Is it also possible to create this with go.Bar so i can see the exact values of each bar?

not_speshal Over a year ago

You can use whatever package you like. This is just the data structure you require.

Collectives™ on Stack Overflow

Visualization of multiple columns

1 Answer 1

2 Comments

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

1 Answer 1

2 Comments

Your Answer

Sign up or log in

Post as a guest

Related