如何在熊猫中绘制特定颜色的散点图？

Question

我正在逐步阅读其他人的代码来学习python数据分析。 我有一个名为df的DataFrame，如下所示：

df.to_dict() 

{'color map': {0: 'green',
  1: 'green',
  2: 'green',
  3: 'green',
  4: 'green',
  5: 'green',
  6: 'green',
  7: 'green',
  8: 'green',
  9: 'green',
  10: 'green',
  11: 'green',
  12: 'green',
  13: 'green',
  14: 'green',
  15: 'green',
  16: 'green',
  17: 'green',
  18: 'green',
  19: 'green',
  20: 'green',
  21: 'green',
  22: 'green',
  23: 'green',
  24: 'green',
  25: 'green',
  26: 'green',
  27: 'green',
  28: 'green',
  29: 'green',
  30: 'green',
  31: 'green',
  32: 'green',
  33: 'green',
  34: 'green',
  35: 'green',
  36: 'green',
  37: 'green',
  38: 'green',
  39: 'green',
  40: 'green',
  41: 'green',
  42: 'green',
  43: 'green',
  44: 'green',
  45: 'green',
  46: 'green',
  47: 'green',
  48: 'green',
  49: 'green',
  50: 'green',
  51: 'green',
  52: 'green',
  53: 'green',
  54: 'green',
  55: 'green',
  56: 'green',
  57: 'green',
  58: 'green',
  59: 'green',
  60: 'green',
  61: 'red',
  62: 'red',
  63: 'red',
  64: 'red',
  65: 'red',
  66: 'red',
  67: 'red',
  68: 'red',
  69: 'red',
  70: 'red',
  71: 'red',
  72: 'red',
  73: 'red',
  74: 'red',
  75: 'red',
  76: 'red',
  77: 'red',
  78: 'red',
  79: 'red',
  80: 'red',
  81: 'red',
  82: 'red',
  83: 'red',
  84: 'red',
  85: 'red',
  86: 'red',
  87: 'red',
  88: 'red',
  89: 'red',
  90: 'red',
  91: 'red',
  92: 'red',
  93: 'red',
  94: 'red',
  95: 'red',
  96: 'red',
  97: 'red',
  98: 'red',
  99: 'red',
  100: 'red',
  101: 'red',
  102: 'red',
  103: 'red',
  104: 'red',
  105: 'red',
  106: 'red',
  107: 'red',
  108: 'red',
  109: 'red',
  110: 'red',
  111: 'red',
  112: 'red',
  113: 'red',
  114: 'red',
  115: 'red',
  116: 'red',
  117: 'red',
  118: 'red',
  119: 'red',
  120: 'red',
  121: 'red',
  122: 'red',
  123: 'red',
  124: 'red',
  125: 'red',
  126: 'red',
  127: 'red',
  128: 'red',
  129: 'red',
  130: 'red',
  131: 'red',
  132: 'red',
  133: 'red',
  134: 'red',
  135: 'red'},
 'name': {0: 'Ivern',
  1: 'Anivia',
  2: 'Xerath',
  3: 'Ahri',
  4: 'Sona',
  5: 'Skarner',
  6: 'Zilean',
  7: 'KogMaw',
  8: 'Janna',
  9: 'Pantheon',
  10: 'Twitch',
  11: 'Annie',
  12: 'Warwick',
  13: 'Draven',
  14: 'Amumu',
  15: 'Xin Zhao',
  16: 'Nunu',
  17: 'Blitzcrank',
  18: 'Yasuo',
  19: 'Leona',
  20: 'Irelia',
  21: 'Ziggs',
  22: 'Tryndamere',
  23: 'Kayle',
  24: 'Miss Fortune',
  25: 'Master Yi',
  26: 'Soraka',
  27: 'Vi',
  28: 'Teemo',
  29: 'Brand',
  30: 'Shaco',
  31: 'Darius',
  32: 'VelKoz',
  33: 'Illaoi',
  34: 'Aatrox',
  35: 'Riven',
  36: 'Jarvan IV',
  37: 'Karthus',
  38: 'Twisted Fate',
  39: 'Jinx',
  40: 'Sejuani',
  41: 'Jax',
  42: 'Caitlyn',
  43: 'Fiddlesticks',
  44: 'Swain',
  45: 'Kindred',
  46: 'Wukong',
  47: 'Taric',
  48: 'Kled',
  49: 'Lulu',
  50: 'Yorick',
  51: 'Thresh',
  52: 'Kassadin',
  53: 'Morgana',
  54: 'Fiora',
  55: 'Renekton',
  56: 'Fizz',
  57: 'Lucian',
  58: 'Hecarim',
  59: 'Bard',
  60: 'Malphite',
  61: 'Vayne',
  62: 'Nami',
  63: 'Orianna',
  64: 'Nocturne',
  65: 'Shyvana',
  66: 'Katarina',
  67: 'Garen',
  68: 'Jayce',
  69: 'Ashe',
  70: 'Sion',
  71: 'Braum',
  72: 'Heimerdinger',
  73: 'Syndra',
  74: 'Tristana',
  75: 'Rammus',
  76: 'Zac',
  77: 'Lux',
  78: 'Zyra',
  79: 'Evelynn',
  80: 'Varus',
  81: 'Ekko',
  82: 'KhaZix',
  83: 'Alistar',
  84: 'Elise',
  85: 'Volibear',
  86: 'Veigar',
  87: 'Taliyah',
  88: 'Sivir',
  89: 'Gangplank',
  90: 'Talon',
  91: 'Galio',
  92: 'Xayah',
  93: 'Diana',
  94: 'Akali',
  95: 'Singed',
  96: 'Rumble',
  97: 'DrMundo',
  98: 'Maokai',
  99: 'Kalista',
  100: 'Gragas',
  101: 'Shen',
  102: 'Aurelion Sol',
  103: 'ChoGath',
  104: 'Lee Sin',
  105: 'Cassiopeia',
  106: 'Nautilus',
  107: 'Lissandra',
  108: 'Udyr',
  109: 'Graves',
  110: 'Olaf',
  111: 'Corki',
  112: 'Quinn',
  113: 'RekSai',
  114: 'Malzahar',
  115: 'Karma',
  116: 'Viktor',
  117: 'Nasus',
  118: 'Vladimir',
  119: 'Trundle',
  120: 'Tahm Kench',
  121: 'Gnar',
  122: 'Kennen',
  123: 'Urgot',
  124: 'Mordekaiser',
  125: 'Camille',
  126: 'Nidalee',
  127: 'LeBlanc',
  128: 'Zed',
  129: 'Jhin',
  130: 'Poppy',
  131: 'Rengar',
  132: 'Rakan',
  133: 'Ezreal',
  134: 'Azir',
  135: 'Ryze'},
 'total matches': {0: 6671,
  1: 6433,
  2: 5108,
  3: 30841,
  4: 11847,
  5: 1720,
  6: 4797,
  7: 6440,
  8: 21182,
  9: 8872,
  10: 18273,
  11: 11585,
  12: 15418,
  13: 16427,
  14: 10853,
  15: 12106,
  16: 4252,
  17: 18545,
  18: 24173,
  19: 15019,
  20: 9350,
  21: 6575,
  22: 11658,
  23: 5129,
  24: 12758,
  25: 20382,
  26: 11766,
  27: 9903,
  28: 11482,
  29: 12896,
  30: 9437,
  31: 15022,
  32: 8899,
  33: 5239,
  34: 3350,
  35: 17855,
  36: 7322,
  37: 2353,
  38: 7807,
  39: 21777,
  40: 9558,
  41: 16466,
  42: 47566,
  43: 4839,
  44: 5873,
  45: 2651,
  46: 6084,
  47: 2877,
  48: 6360,
  49: 20821,
  50: 3531,
  51: 39734,
  52: 12828,
  53: 18794,
  54: 15468,
  55: 15130,
  56: 17665,
  57: 46914,
  58: 7793,
  59: 10422,
  60: 8393,
  61: 28374,
  62: 15006,
  63: 23636,
  64: 4888,
  65: 4327,
  66: 9929,
  67: 7603,
  68: 7961,
  69: 18461,
  70: 3829,
  71: 12090,
  72: 3172,
  73: 14887,
  74: 10308,
  75: 4337,
  76: 9085,
  77: 21329,
  78: 9576,
  79: 6171,
  80: 7826,
  81: 15482,
  82: 16006,
  83: 7302,
  84: 11746,
  85: 4216,
  86: 9657,
  87: 4534,
  88: 5595,
  89: 13843,
  90: 5802,
  91: 10215,
  92: 23525,
  93: 5506,
  94: 8428,
  95: 2560,
  96: 6244,
  97: 3220,
  98: 10890,
  99: 5470,
  100: 17360,
  101: 8289,
  102: 1708,
  103: 5815,
  104: 48457,
  105: 5333,
  106: 10392,
  107: 3580,
  108: 3869,
  109: 19035,
  110: 5020,
  111: 1927,
  112: 2840,
  113: 1714,
  114: 4680,
  115: 20065,
  116: 5512,
  117: 8114,
  118: 9611,
  119: 3305,
  120: 4076,
  121: 4978,
  122: 7369,
  123: 780,
  124: 1603,
  125: 3690,
  126: 4520,
  127: 8690,
  128: 17687,
  129: 11240,
  130: 5165,
  131: 6590,
  132: 18339,
  133: 23480,
  134: 2597,
  135: 4607},
 'win rate': {0: 55.868685354519563,
  1: 53.971708378672467,
  2: 53.621769772905246,
  3: 53.545604876625276,
  4: 53.135814974255091,
  5: 53.081395348837212,
  6: 52.991452991452995,
  7: 52.717391304347828,
  8: 52.714568973656881,
  9: 52.569882777276824,
  10: 52.448968423356867,
  11: 52.447129909365565,
  12: 52.34141912050849,
  13: 52.31022097765873,
  14: 52.308117571178478,
  15: 52.288121592598714,
  16: 52.187206020696145,
  17: 51.997843084389331,
  18: 51.847102138749847,
  19: 51.821026699513951,
  20: 51.732620320855617,
  21: 51.665399239543731,
  22: 51.655515525819183,
  23: 51.511015792552151,
  24: 51.504938078068662,
  25: 51.47679324894515,
  26: 51.393846676865543,
  27: 51.267292739573868,
  28: 51.175753353074384,
  29: 51.155397022332508,
  30: 51.139133199109885,
  31: 51.098389029423508,
  32: 51.084391504663444,
  33: 51.078450085894254,
  34: 51.044776119402989,
  35: 51.038924670960519,
  36: 51.010652827096422,
  37: 50.998725031874201,
  38: 50.877417702062253,
  39: 50.870184139229458,
  40: 50.81607030759573,
  41: 50.765213166524958,
  42: 50.758945465248281,
  43: 50.692291795825582,
  44: 50.689596458368804,
  45: 50.660128253489248,
  46: 50.641025641025635,
  47: 50.538755648244702,
  48: 50.518867924528301,
  49: 50.51150280966332,
  50: 50.467289719626166,
  51: 50.432878642975787,
  52: 50.342999688182097,
  53: 50.313929977652435,
  54: 50.297388156193435,
  55: 50.257766027759423,
  56: 50.195301443532415,
  57: 50.108709553651366,
  58: 50.096240215578078,
  59: 50.076760698522364,
  60: 50.065530799475752,
  61: 49.904842461408336,
  62: 49.886711981873916,
  63: 49.87307497038416,
  64: 49.836333878887068,
  65: 49.826669748093366,
  66: 49.803605599758285,
  67: 49.78298040247271,
  68: 49.780178369551564,
  69: 49.77520177671849,
  70: 49.751893444763645,
  71: 49.75186104218362,
  72: 49.716267339218156,
  73: 49.586887888762007,
  74: 49.582848273185874,
  75: 49.550380447313813,
  76: 49.532195927352781,
  77: 49.510056730273341,
  78: 49.498746867167917,
  79: 49.473343056230753,
  80: 49.373881932021469,
  81: 49.302415708564787,
  82: 49.294014744470822,
  83: 49.274171459874005,
  84: 49.267835859015833,
  85: 49.217267552182165,
  86: 49.166407787097441,
  87: 49.117776797529778,
  88: 49.097408400357459,
  89: 49.07895687351008,
  90: 49.052051016890729,
  91: 49.045521292217323,
  92: 48.875664187035071,
  93: 48.873955684707596,
  94: 48.837209302325576,
  95: 48.828125,
  96: 48.814862267777066,
  97: 48.633540372670808,
  98: 48.558310376492194,
  99: 48.555758683729437,
  100: 48.513824884792626,
  101: 48.498009410061528,
  102: 48.419203747072601,
  103: 48.40928632846088,
  104: 48.360402005902145,
  105: 48.340521282580163,
  106: 48.325635103926096,
  107: 48.324022346368714,
  108: 48.307056086844149,
  109: 48.205936432886787,
  110: 48.167330677290835,
  111: 48.157758173326414,
  112: 48.133802816901408,
  113: 48.133022170361727,
  114: 48.119658119658119,
  115: 48.11363070022427,
  116: 48.040638606676346,
  117: 48.015775203352227,
  118: 47.997086671522219,
  119: 47.927382753403933,
  120: 47.76741903827282,
  121: 47.609481719566091,
  122: 47.414845976387568,
  123: 47.307692307692307,
  124: 47.286338116032439,
  125: 46.720867208672082,
  126: 46.637168141592923,
  127: 46.628308400460298,
  128: 46.429581048227512,
  129: 46.387900355871885,
  130: 46.331074540174249,
  131: 45.720789074355082,
  132: 45.591362669720269,
  133: 45.468483816013631,
  134: 44.050827878321144,
  135: 38.897330149772088}}

我想绘制一个散点图，其中total matches为x轴，win rate为y轴，每个点都以color map中的相应颜色着色。

像这样（不考虑名称描述）：

代码是：

df.plot(kind='scatter', x='total matches', y='win rate', color=df['color map'].tolist(), figsize=(15,10), title='win rate vs # matches by champions')

但这次加薪ValueError: could not convert string to float: 'green' 我该如何解决？

Answer 1

此参数语法似乎有效。

鉴于 ID PRODUCT PRICE P_DATE PRICE_DIFF ---------- ------- ---------- --------- ---------- 1 cheese 1000 11-DEC-17 100 2 onion 500 11-DEC-17 50 ：

df

使用：

     name  total_matches  win_rate color_map
0   Ivern           6671     55.87     green
1  Anivia           6433     53.97       red
2  Xerath           5108     53.62      blue
3    Ahri          30841     53.55    yellow
4    Sona          11847     53.14     black

Answer 2

此代码将获得您正在寻找的内容，而无需将整个数据框转换为字典。但是，我必须将每个Pandas列转换为列表，以便为每个点添加名称标签，但如果没有名称标签，则可以直接使用数据框。

# All the necessary imports
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import style

# There are several style templates in Matplotlib, this one seems to fit your look
style.use('fivethirtyeight')

# Reduce font size to reduce clutter
plt.rcParams['font.size'] = 6.0
fig, ax = plt.subplots()

# Create list from each dataframe column
x = df['total matches'].tolist()
y = df['win rate'].tolist()
colors = df['color map'].tolist()
names = df['name'].tolist()

# Create scatter plot, c property takes list of colors
ax.scatter(x, y, c=colors)

# Iterate through names to add label to each point
for i, txt in enumerate(names):
    ax.annotate(txt, (x[i],y[i]))

# Show the chart, matplotlib can save the chart in several formats as well
plt.show()

Answer 3

这应该归功于我的熊猫版本。当我更新我的大熊猫时，从0.19.2到0.21.1。错误消失了。