Python 使用pandas在不同轴上绘制条形图和时间序列

我有一个熊猫数据框，它是从具有以下结构的.csv文件中读取的：

Date,       Latitude,   Longitude,        Brand,        Pump, AKI,  Trip Miles,  Total Miles, Gallons,  MPG,    PPG,    Total,  Tires,  MPG-D,
11/03/2013, 40° 1.729', -105° 15.516',    Boulder Gas,  2,    87,   134.3,       134.3,       6.563,    20.46,  3.319,  21.78,  Stock,  ,
11/17/2013, 40° 1.729', -105° 15.516',    Boulder Gas,  2,    87,   161.8,       296.0,       7.467,    21.67,  3.279,  24.48,  Stock,  ,
11/27/2013, 40° 0.872', -105° 12.775',    Buffalo Gas,  6,    87,   180.8,       477.0,       8.096,    22.33,  3.359,  27.19,  Stock,  ,
12/07/2013, 40° 1.729', -105° 15.516',    Boulder Gas,  6,    87,   265.1,       742.0,       12.073,   21.96,  3.179,  38.38,  Stock,  ,
12/11/2013, 40° 2.170', -105° 15.522',    Circle K,     4,    87,   240.9,       983.0,       9.868,    24.41,  3.179,  31.37,  Stock,  ,
12/15/2013, 40° 8.995', -105° 7.876',     Shell,        3,    87,   188.7,       1172,        8.596,    21.95,  3.059,  26.30,  ,       ,
12/21/2013, 40° 1.770', -105° 15.481',    Conoco,       3,    87,   113.8,       1286,        5.517,    20.62,  3.139,  17.32,  Winter, ,
01/09/2014, 40° 1.729', -105° 15.516',    Boulder Gas,  2,    87,   139.5,       1426,        7.181,    19.42,  3.279,  23.55,  Winter, 21.3,
01/13/2013, 40° 1.770', -105° 15.481',    Conoco,       7,    87,   260.8,       1688,        11.177,   23.33,  3.239,  36.20,  Winter, 25.5,
01/18/2014, 40° 1.729', -105° 15.516',    Boulder Gas,  2,    87,   102.0,       1790,        4.401,    23.18,  3.239,  14.26,  Winter, 25.5,
02/02/2014, 39° 59.132', -105° 14.962',   King Soopers, 5,    87,   175.3,       1965,        8.436,    20.78,  3.019,  25.47,  Winter, 24.0,
02/03/2014, 40° 1.770', -105° 15.481',    Conoco,       3,    87,   249.9,       2215,        10.452,   23.91,  3.219,  33.64,  Winter, 25.2,
02/08/2014, 40° 2.170', -105° 15.522',    Circle K,     7,    87,   186.4,       2402,        8.565,    21.76,  3.239,  27.74,  Winter, 24.3,
02/13/2014, 40° 1.729', -105° 15.516',    Boulder Gas,  8,    87,    79.6,       2481,        4.125,    19.30,  3.439,  14.19,  Winter, 21.3,
03/06/2014, 40.014460, -105.225034,       Conoco,       5,    87,   172.4,       2654,        8.618,    20.00,  3.779,  32.57,  Winter, 21.9,
03/09/2014, 40.029498, -105.258117,       Conoco,       6,    87,   230.4,       2884,        9.016,    25.55,  3.759,  33.89,  Winter, 27.3,
03/17/2014, 40.036236, -105.258763,       Conoco,       6,    87,   130.1,       3014,        5.368,    24.24,  3.719,  19.96,  Winter, 25.8,
03/24/2014, 40.036236, -105.258763,       Conoco,       1,    87,   282.3,       3297,       11.540,    24.46,  3.719,  42.92,  Winter, 27.3,

我想生成一个图，其中x轴是日期，左y轴是英里/加仑，右y轴是英里。在这个图中，我想用一种颜色显示“MPG”列的时间序列，用另一种颜色显示“MPG-D”列的时间序列，用第三种颜色显示“旅行里程”列的条形图

我一直试图遵循下面的代码，但它会生成一个条形图和两个时间序列图，其中所有内容都在同一个轴上，并且没有显示y标签

%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt

data = pd.read_csv('mpg.csv', skipinitialspace=True,index_col='Date')
plt.figure()
ax = data['Trip Miles'].plot(kind='bar',secondary_y=['Trip Miles'])
ax.right_ax.set_ylabel('Miles')
ax.set_ylabel('Miles/Gallon')
data['MPG'].plot()
data['MPG-D'].plot()

---

您需要更明确地指定轴。试着这样做：

%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt

fig, tsax = plt.subplots()
barax = tsax.twinx()

data = pd.read_csv('mpg.csv', skipinitialspace=True,index_col='Date')
data['Trip Miles'].plot(kind='bar', ax=barax)
barax.set_ylabel('Miles')
tsax.set_ylabel('Miles/Gallon')
data['MPG'].plot(ax=tsax)
data['MPG-D'].plot(ax=tsax)

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as mgrid
import pandas as pd

fig, tsax = plt.subplots(figsize=(12,5))
barax = tsax.twinx()

data = pd.DataFrame(np.random.randn(10,3), columns=list('ABC'), index=pd.DatetimeIndex(freq='1M', start='2012-01-01', periods=10))
data['A'] **= 2

# the `width` is specified in days -- adjust for your data
barax.bar(data.index, data['A'], width=5, facecolor='indianred')

barax.set_ylabel('Miles')
tsax.set_ylabel('Miles/Gallon')

barax.xaxis.tick_top()

fig.tight_layout()

tsax.plot(data.index, data['B'])
tsax.plot(data.index, data['C'])

编辑 所以这里的一个大问题是，熊猫条形图和直线图以根本不同的方式格式化x轴。具体来说，条形图试图为每个条形图制作带有刻度和标签的定性刻度。但在这里，您似乎对一个更像典型时间序列的格式感兴趣

所以这里我建议你忘记双轴图表。相反，只需在两个完全分离的轴上绘图。像这样：

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as mgrid
import pandas as pd

fig = plt.figure(figsize=(12,5))
grid = mgrid.GridSpec(nrows=2, ncols=1, height_ratios=[2, 1])

barax = fig.add_subplot(grid[0])
tsax = fig.add_subplot(grid[1])
data = pd.DataFrame(np.random.randn(10,3), columns=list('ABC'), index=pd.DatetimeIndex(freq='1M', start='2012-01-01', periods=10))

data['A'] **= 2
data['A'].plot(ax=barax, style='o--')
barax.set_ylabel('Miles')
tsax.set_ylabel('Miles/Gallon')

barax.xaxis.tick_top()

data['B'].plot(ax=tsax)
data['C'].plot(ax=tsax)
fig.tight_layout()

这给了我：

但是，如果您确实需要条形图，或者您确实希望所有内容都位于同一个双x轴上，则必须使用matplotlib的API进行如下打印：

%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt

fig, tsax = plt.subplots()
barax = tsax.twinx()

data = pd.read_csv('mpg.csv', skipinitialspace=True,index_col='Date')
data['Trip Miles'].plot(kind='bar', ax=barax)
barax.set_ylabel('Miles')
tsax.set_ylabel('Miles/Gallon')
data['MPG'].plot(ax=tsax)
data['MPG-D'].plot(ax=tsax)

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as mgrid
import pandas as pd

fig, tsax = plt.subplots(figsize=(12,5))
barax = tsax.twinx()

data = pd.DataFrame(np.random.randn(10,3), columns=list('ABC'), index=pd.DatetimeIndex(freq='1M', start='2012-01-01', periods=10))
data['A'] **= 2

# the `width` is specified in days -- adjust for your data
barax.bar(data.index, data['A'], width=5, facecolor='indianred')

barax.set_ylabel('Miles')
tsax.set_ylabel('Miles/Gallon')

barax.xaxis.tick_top()

fig.tight_layout()

tsax.plot(data.index, data['B'])
tsax.plot(data.index, data['C'])

这就给了我

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如何让它沿x轴显示日期？@DeltaP您可以发布更多行数据吗？请删掉我们不需要的栏目，并保持csv格式。我已经包括了整个文件。我宁愿不修剪那些不必要的栏目，因为它们是其他事情所必需的。就csv格式而言…我想你是想删除空格？这真的没有必要，因为我遇到的所有函数都有删除初始空格的方法。@DeltaP这里的区别在于，SO与实现最终结果无关，只是通知您如何实现最终结果。因此，出于这些目的，最好包括一个最小的工作示例，aka，正如我在示例中所做的那样。