在海运中绘制单独的组时，如何将数据作为一个组包括在内
问题描述
我有一个数据框，我必须比较具有来自我的数据框(过滤)的特定值的列的中值与具有原始数据框中的所有值的相同列的中值。
这是我所达到的最大限度，我已经提供了两个图表，我认为这两个图表应该在同一个图表中：
我的目标是将这两个图表合并到一个图表中。
以下是我提供该输出的代码。
filt_waterfront = df['waterfront'] == 1
fig, axs = plt.subplots(1,2)
sns.boxplot(y='price', data = df[filt_waterfront], ax=axs[0], color= 'red')
sns.boxplot(y='price', data = df, ax=axs[1], color = 'orange')
fig.set_size_inches(9,6)
fig.suptitle('Price plots of properties with waterfront and general properties')
fig.axes[1].set_ylabel("Price")
fig.axes[0].set_ylabel("Price")
fig.axes[1].set_xlabel("General Properties")
fig.axes[0].set_xlabel("Properties with Waterfront") <br>
我的筛选器是具有滨水的属性，右侧的图表显示常规属性，这意味着原始列，而左侧的筛选器，我正在尝试找到一种方法将这两个图表合并到一个图表中(因为它看起来要干净得多，而且除了我失败之外，没有其他真正的理由显示两个图表)。
如有任何帮助，我们将不胜感激，提前谢谢！
解决方案
- 最简单的方法不需要手动创建x轴和分配不同的框图，而是创建一个单独的DataFrame，其中的所有数据都根据所需的x轴标签进行标记。
- 如果您不想同时使用这两个原始类别，请筛选出要使用的数据：
filtered = df[['price', 'waterfront']][df.waterfront.eq(1)].copy()
- 下面的示例使用OP中的示例DataFrame，它有很多列。
comb是通过选择要绘制的特定列来创建的，以防止创建包含不必要信息的可能较大的DataFrame。- 如果DataFrame已经只有必需的列，则使用
comb = df.assign(waterfront="All")
- 如果DataFrame已经只有必需的列，则使用
import pandas as pd
import seaborn as sns
# Using the sample data from the OP, which has many columns
# Create a copy of the columns to plot and all rows, with waterfront as "All"
comb = df[['price', 'waterfront']].assign(waterfront="All")
# Append it to the original columns with the original categories
data = df[['price', 'waterfront']].append(comb).reset_index(drop=True)
# display(data.head())
price waterfront
0 221900.0 0
1 538000.0 1
2 180000.0 0
3 604000.0 1
4 510000.0 0
# display(data.tail())
price waterfront
95 488000.0 All
96 210490.0 All
97 785000.0 All
98 450000.0 All
99 1350000.0 All
# plot
sns.boxplot(data=data, y='price', x='waterfront')
示例数据
data = {'id': [7129300520, 6414100192, 5631500400, 2487200875, 1954400510, 7237550310, 1321400060, 2008000270, 2414600126, 3793500160, 1736800520, 9212900260, 114101516, 6054650070, 1175000570, 9297300055, 1875500060, 6865200140, 16000397, 7983200060, 6300500875, 2524049179, 7137970340, 8091400200, 3814700200, 1202000200, 1794500383, 3303700376, 5101402488, 1873100390, 8562750320, 2426039314, 461000390, 7589200193, 7955080270, 9547205180, 9435300030, 2768000400, 7895500070, 2078500320, 5547700270, 7766200013, 7203220400, 9270200160, 1432701230, 8035350320, 8945200830, 4178300310, 9215400105, 822039084], 'date': ['20141013T000000', '20141209T000000', '20150225T000000', '20141209T000000', '20150218T000000', '20140512T000000', '20140627T000000', '20150115T000000', '20150415T000000', '20150312T000000', '20150403T000000', '20140527T000000', '20140528T000000', '20141007T000000', '20150312T000000', '20150124T000000', '20140731T000000', '20140529T000000', '20141205T000000', '20150424T000000', '20140514T000000', '20140826T000000', '20140703T000000', '20140516T000000', '20141120T000000', '20141103T000000', '20140626T000000', '20141201T000000', '20140624T000000', '20150302T000000', '20141110T000000', '20141201T000000', '20140624T000000', '20141110T000000', '20141203T000000', '20140613T000000', '20140528T000000', '20141230T000000', '20150213T000000', '20140620T000000', '20140715T000000', '20140811T000000', '20140707T000000', '20141028T000000', '20140729T000000', '20140718T000000', '20150325T000000', '20140716T000000', '20150428T000000', '20150311T000000'], 'price': [221900.0, 538000.0, 180000.0, 604000.0, 510000.0, 1225000.0, 257500.0, 291850.0, 229500.0, 323000.0, 662500.0, 468000.0, 310000.0, 400000.0, 530000.0, 650000.0, 395000.0, 485000.0, 189000.0, 230000.0, 385000.0, 2000000.0, 285000.0, 252700.0, 329000.0, 233000.0, 937000.0, 667000.0, 438000.0, 719000.0, 580500.0, 280000.0, 687500.0, 535000.0, 322500.0, 696000.0, 550000.0, 640000.0, 240000.0, 605000.0, 625000.0, 775000.0, 861990.0, 685000.0, 309000.0, 488000.0, 210490.0, 785000.0, 450000.0, 1350000.0], 'bedrooms': [3, 3, 2, 4, 3, 4, 3, 3, 3, 3, 3, 2, 3, 3, 5, 4, 3, 4, 2, 3, 4, 3, 5, 2, 3, 3, 3, 3, 3, 4, 3, 2, 4, 3, 4, 3, 4, 4, 4, 4, 4, 4, 5, 3, 3, 3, 3, 4, 3, 3], 'bathrooms': [1.0, 2.25, 1.0, 3.0, 2.0, 4.5, 2.25, 1.5, 1.0, 2.5, 2.5, 1.0, 1.0, 1.75, 2.0, 3.0, 2.0, 1.0, 1.0, 1.0, 1.75, 2.75, 2.5, 1.5, 2.25, 2.0, 1.75, 1.0, 1.75, 2.5, 2.5, 1.5, 1.75, 1.0, 2.75, 2.5, 1.0, 2.0, 1.0, 2.5, 2.5, 2.25, 2.75, 1.0, 1.0, 2.5, 1.0, 2.5, 1.75, 2.5], 'sqmeters_living': [109.624675, 238.758826, 71.534745, 182.088443, 156.075808, 503.530286, 159.327388, 98.476403, 165.366035, 175.585284, 330.73207, 107.76663, 132.850242, 127.276106, 168.153103, 274.061687, 175.585284, 148.643627, 111.48272, 116.127834, 150.501672, 283.351914, 210.888146, 99.405425, 227.610554, 158.862876, 227.610554, 130.063174, 141.211446, 238.758826, 215.533259, 110.553698, 216.462282, 101.263471, 191.37867, 213.675214, 154.217763, 219.24935, 113.340766, 243.403939, 238.758826, 392.047566, 333.983649, 145.856559, 118.914902, 293.571163, 91.973244, 212.746191, 116.127834, 255.759941], 'sqmeters_lot': [524.897808, 672.798216, 929.022668, 464.511334, 750.650316, 9469.528056, 633.500557, 902.173913, 693.979933, 609.43887, 910.070606, 557.413601, 1848.848012, 899.293943, 450.575994, 464.511334, 1304.347826, 399.479747, 915.087328, 908.026756, 462.653289, 4168.246005, 585.284281, 895.856559, 603.864734, 436.361947, 250.0, 146.878484, 592.716462, 666.38796, 369.751022, 117.521368, 464.511334, 278.7068, 618.636195, 284.280936, 3237.458194, 557.413601, 750.185805, 701.690821, 512.820513, 2246.934225, 523.875883, 211.817168, 897.064288, 1263.749535, 792.270531, 1246.376812, 553.976217, 6039.111854], 'floors': [1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.5, 1.0, 1.5, 2.0, 2.0, 1.5, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.5, 2.0, 1.5, 1.0, 2.0, 2.0, 3.0, 1.5, 1.5, 1.0, 1.5, 1.0, 2.0, 1.0, 2.0, 2.0, 1.0, 2.0, 2.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0], 'waterfront': [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1], 'view': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], 'grade': [7, 7, 6, 7, 8, 11, 7, 7, 7, 7, 8, 7, 7, 7, 7, 9, 7, 7, 7, 7, 7, 9, 8, 7, 8, 6, 8, 8, 7, 8, 8, 7, 7, 8, 7, 8, 5, 8, 7, 8, 9, 8, 9, 7, 6, 8, 6, 9, 7, 9], 'sqmeters_above': [109.624675, 201.597919, 71.534745, 97.54738, 156.075808, 361.389818, 159.327388, 98.476403, 97.54738, 175.585284, 172.798216, 79.895949, 132.850242, 127.276106, 168.153103, 183.946488, 175.585284, 148.643627, 111.48272, 116.127834, 79.895949, 216.462282, 210.888146, 99.405425, 227.610554, 158.862876, 162.578967, 130.063174, 73.392791, 238.758826, 215.533259, 110.553698, 140.282423, 101.263471, 118.914902, 140.282423, 86.399108, 219.24935, 82.683017, 243.403939, 238.758826, 241.545894, 333.983649, 145.856559, 85.470085, 293.571163, 91.973244, 212.746191, 116.127834, 201.133408], 'sqmeters_basement': [0.0, 37.160907, 0.0, 84.541063, 0.0, 142.140468, 0.0, 0.0, 67.818655, 0.0, 157.933854, 27.87068, 0.0, 0.0, 0.0, 90.115199, 0.0, 0.0, 0.0, 0.0, 70.605723, 66.889632, 0.0, 0.0, 0.0, 0.0, 65.031587, 0.0, 67.818655, 0.0, 0.0, 0.0, 76.179859, 0.0, 72.463768, 73.392791, 67.818655, 0.0, 30.657748, 0.0, 0.0, 150.501672, 0.0, 0.0, 33.444816, 0.0, 0.0, 0.0, 0.0, 0.0], 'yr_built': [1955.0, 1951.0, 1933.0, 1965.0, 1987.0, 2001.0, 1995.0, 1963.0, 1960.0, 2003.0, 1965.0, 1942.0, 1927.0, 1977.0, 1900.0, 1979.0, 1994.0, 1916.0, 1921.0, 1969.0, 1947.0, 1968.0, 1995.0, 1985.0, 1985.0, 1941.0, 1915.0, 1909.0, 1948.0, 2005.0, 2003.0, 2005.0, 1929.0, 1929.0, 1981.0, 1930.0, 1933.0, 1904.0, 1969.0, 1996.0, 2000.0, 1984.0, 2014.0, 1922.0, 1959.0, 2003.0, 1966.0, 1981.0, 1953.0, 0.0], 'yr_renovated': [0.0, 1991.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2002.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'zipcode': [98178.0, 98125.0, 98028.0, 98136.0, 98074.0, 98053.0, 98003.0, 98198.0, 98146.0, 98038.0, 98007.0, 98115.0, 98028.0, 98074.0, 98107.0, 98126.0, 98019.0, 98103.0, 98002.0, 98003.0, 98133.0, 98040.0, 98092.0, 98030.0, 98030.0, 98002.0, 98119.0, 98112.0, 98115.0, 98052.0, 98027.0, 98133.0, 98117.0, 98117.0, 98058.0, 98115.0, 98052.0, 98107.0, 98001.0, 98056.0, 98074.0, 98166.0, 98053.0, 98119.0, 98058.0, 98019.0, 98023.0, 98007.0, 98115.0, 0.0], 'lat': [47.5112, 47.721, 47.7379, 47.5208, 47.6168, 47.6561, 47.3097, 47.4095, 47.5123, 47.3684, 47.6007, 47.69, 47.7558, 47.6127, 47.67, 47.5714, 47.7277, 47.6648, 47.3089, 47.3343, 47.7025, 47.5316, 47.3266, 47.3533, 47.3739, 47.3048, 47.6386, 47.6221, 47.695, 47.7073, 47.5391, 47.7274, 47.6823, 47.6889, 47.4276, 47.6827, 47.6621, 47.6702, 47.3341, 47.5301, 47.6145, 47.445, 47.6848, 47.6413, 47.4485, 47.7443, 47.3066, 47.6194, 47.6796, 0.0], 'long': [-122.257, -122.319, -122.233, -122.393, -122.045, -122.005, -122.327, -122.315, -122.337, -122.031, -122.145, -122.292, -122.229, -122.045, -122.394, -122.375, -121.962, -122.343, -122.21, -122.306, -122.341, -122.233, -122.169, -122.166, -122.172, -122.218, -122.36, -122.314, -122.304, -122.11, -122.07, -122.357, -122.368, -122.375, -122.157, -122.31, -122.132, -122.362, -122.282, -122.18, -122.027, -122.347, -122.016, -122.364, -122.175, -121.977, -122.371, -122.151, -122.301, 0.0], 'sqmeters_living15': [124.489038, 157.004831, 252.694166, 126.347083, 167.22408, 442.21479, 207.915273, 153.28874, 165.366035, 222.036418, 205.31401, 123.560015, 165.366035, 127.276106, 126.347083, 198.810851, 175.585284, 149.57265, 98.476403, 118.914902, 130.063174, 381.828317, 208.101078, 113.340766, 204.384987, 95.689335, 163.50799, 172.798216, 141.211446, 244.332962, 239.687848, 129.134151, 135.63731, 145.856559, 187.662579, 147.714604, 200.668896, 160.720922, 119.843924, 243.403939, 229.468599, 223.894463, 336.770717, 146.785582, 124.489038, 283.351914, 114.083984, 248.978075, 90.115199, 0.0], 'sqmeters_lot15': [524.897808, 709.680416, 748.978075, 464.511334, 697.045708, 9469.528056, 633.500557, 902.173913, 753.716091, 703.27016, 829.152731, 557.413601, 1179.580082, 948.34634, 450.575994, 371.609067, 1302.303976, 399.479747, 473.337049, 822.185061, 462.653289, 1889.260498, 650.780379, 779.07841, 637.774062, 437.105165, 331.939799, 358.695652, 579.245634, 559.82906, 369.751022, 163.136381, 464.511334, 471.943515, 810.107767, 303.232999, 1065.310294, 436.640654, 724.637681, 1104.050539, 526.662951, 2844.388703, 523.875883, 245.261984, 818.283166, 857.673727, 821.256039, 1271.367521, 473.801561, 0.0]}
df = pd.DataFrame(data)
# display(df[['price', 'waterfront']].head())
price waterfront
0 221900.0 0
1 538000.0 1
2 180000.0 0
3 604000.0 1
4 510000.0 0
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