Here is my dataframe:
这是我的数据帧：
jan f m a m j \
2000 -7.894737 22.387006 22.077922 14.5455 15.8038 -3.33333
2001 -3.578947 11.958763 28.741093 5.05415 74.7151 11.2426
2002 -24.439661 -2.570483 1.810242 8.56044 84.5474 -26.9753
2003 14.410453 -10.106570 8.179654 -11.6469 -15.0022 -13.9757
2004 -3.978623 -13.280310 2.558639 -1.13076 12.7156 -4.47235
2005 2.018146 1.385053 9.461930 14.1947 -10.4865 -11.1553
2006 -6.528617 -5.506220 -2.054323 1.39073 7.74041 -0.328937
2007 -1.634891 8.923088 4.951521 -1.33334 3.82215 7.69231
2008 20.539609 0.132377 -3.117323 6.53806 9.99998 16.1356
2009 -3.979917 -9.342541 -23.233634 -26.5963 -27.0396 -4.39528
2010 6.141145 5.304527 -4.722650 4.32727 -4.55749 -3.98345
2011 -1286.639676 16.295265 -13.697203 89.2141 12.4599 -2.56771
2012 1.939279 -6.047198 -273.852729 -2.06906 9.35551 -327.816
2013 5.361207 -0.341469 93.825888 -4.90762 61.0443 3.89917
2014 7.900937 65.372284 65.955447 -8.5217 8.12922 6.99473
2015 -116.635830 -1.094263 96.942247 -6308.42 -1.05717 1.70411
2016 67.714100 -8.219712 2806.000000 nr nr nr
The index values should come on x axis and the column names need to be displayed on y axis. How can I achieve that?
索引值应位于x轴上，列名称需要显示在y轴上。我怎样才能做到这一点？
1 个解决方案
#1
13
That is straightforward using seaborn; I demonstrate how to do it using random data, so all you have to do is to replace data in the example below by your actual dataframe.
使用seaborn很简单;我演示了如何使用随机数据来完成它，因此您所要做的就是用实际数据帧替换下面示例中的数据。
My dataframe looks like this:
我的数据框看起来像这样：
A B C D E
2000 0.722553 0.948447 0.598707 0.656252 0.618292
2001 0.920532 0.054941 0.909858 0.721002 0.222167
2002 0.048496 0.963871 0.689730 0.697573 0.349308
2003 0.692897 0.272768 0.581736 0.150674 0.861672
2004 0.889694 0.658286 0.879855 0.739821 0.010971
2005 0.937347 0.132955 0.704528 0.443084 0.552123
2006 0.869499 0.750177 0.675160 0.873720 0.270204
2007 0.156933 0.186630 0.371993 0.153790 0.397232
2008 0.384696 0.585156 0.746883 0.185457 0.095387
2009 0.667236 0.340058 0.446081 0.863402 0.227776
2010 0.817394 0.343427 0.804157 0.245394 0.850774
The output then looks as follows (please note that the index is at the x-axis and the column names at the y-axis as requested):
然后输出如下（请注意索引位于x轴，y轴的列名称按要求）：
Here is the entire code with some inline comments:
以下是包含一些内联注释的完整代码：
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
# create some random data; replace that by your actual dataset
data = pd.DataFrame(np.random.rand(11, 5), columns=['A', 'B', 'C', 'D', 'E'], index = range(2000, 2011, 1))
# plot heatmap
ax = sns.heatmap(data.T)
# turn the axis label
for item in ax.get_yticklabels():
item.set_rotation(0)
for item in ax.get_xticklabels():
item.set_rotation(90)
# save figure
plt.savefig('seabornPandas.png', dpi=100)
plt.show()
#1
13
That is straightforward using seaborn; I demonstrate how to do it using random data, so all you have to do is to replace data in the example below by your actual dataframe.
使用seaborn很简单;我演示了如何使用随机数据来完成它，因此您所要做的就是用实际数据帧替换下面示例中的数据。
My dataframe looks like this:
我的数据框看起来像这样：
A B C D E
2000 0.722553 0.948447 0.598707 0.656252 0.618292
2001 0.920532 0.054941 0.909858 0.721002 0.222167
2002 0.048496 0.963871 0.689730 0.697573 0.349308
2003 0.692897 0.272768 0.581736 0.150674 0.861672
2004 0.889694 0.658286 0.879855 0.739821 0.010971
2005 0.937347 0.132955 0.704528 0.443084 0.552123
2006 0.869499 0.750177 0.675160 0.873720 0.270204
2007 0.156933 0.186630 0.371993 0.153790 0.397232
2008 0.384696 0.585156 0.746883 0.185457 0.095387
2009 0.667236 0.340058 0.446081 0.863402 0.227776
2010 0.817394 0.343427 0.804157 0.245394 0.850774
The output then looks as follows (please note that the index is at the x-axis and the column names at the y-axis as requested):
然后输出如下（请注意索引位于x轴，y轴的列名称按要求）：
Here is the entire code with some inline comments:
以下是包含一些内联注释的完整代码：
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
# create some random data; replace that by your actual dataset
data = pd.DataFrame(np.random.rand(11, 5), columns=['A', 'B', 'C', 'D', 'E'], index = range(2000, 2011, 1))
# plot heatmap
ax = sns.heatmap(data.T)
# turn the axis label
for item in ax.get_yticklabels():
item.set_rotation(0)
for item in ax.get_xticklabels():
item.set_rotation(90)
# save figure
plt.savefig('seabornPandas.png', dpi=100)
plt.show()