使用Seaborn扩大热图的大小 [英] Make the size of a heatmap bigger with seaborn
本文介绍了使用Seaborn扩大热图的大小的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我创建了Seaborn的热图
I create a heatmap with seaborn
df1.index = pd.to_datetime(df1.index)
df1 = df1.set_index('TIMESTAMP')
df1 = df1.resample('30min').mean()
ax = sns.heatmap(df1.iloc[:, 1:6:], annot=True, linewidths=.5)
但是问题是，当数据帧中的数据很多时，热图将变得太小，并且内部值开始不像附加的图像中那样清晰.
But the probleme is when there is lot of data in the dataframe the heatmap will be too small and the value inside begin not clear like in the attached image.
如何将热图的大小更改为更大? 谢谢
How can I change the size of the heatmap to be more bigger ? Thank you
编辑
我尝试:
df1.index = pd.to_datetime(df1.index)
fig, ax = plt.subplots(figsize=(10,10)) # Sample figsize in inches
sns.heatmap(df1.iloc[:, 1:6:], annot=True, linewidths=.5, ax=ax)
df1 = df1.set_index('TIMESTAMP')
df1 = df1.resample('1d').mean()
ax = sns.heatmap(df1.iloc[:, 1:6:], annot=True, linewidths=.5)
但是我得到这个错误:
KeyError Traceback (most recent call last)
C:\Users\Demonstrator\Anaconda3\lib\site-packages\pandas\indexes\base.py in get_loc(self, key, method, tolerance)
1944 try:
-> 1945 return self._engine.get_loc(key)
1946 except KeyError:
pandas\index.pyx in pandas.index.IndexEngine.get_loc (pandas\index.c:4154)()
pandas\index.pyx in pandas.index.IndexEngine.get_loc (pandas\index.c:4018)()
pandas\hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_item (pandas\hashtable.c:12368)()
pandas\hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_item (pandas\hashtable.c:12322)()
KeyError: 'TIMESTAMP'
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
<ipython-input-779-acaf05718dd8> in <module>()
2 fig, ax = plt.subplots(figsize=(10,10)) # Sample figsize in inches
3 sns.heatmap(df1.iloc[:, 1:6:], annot=True, linewidths=.5, ax=ax)
----> 4 df1 = df1.set_index('TIMESTAMP')
5 df1 = df1.resample('1d').mean()
6 ax = sns.heatmap(df1.iloc[:, 1:6:], annot=True, linewidths=.5)
C:\Users\Demonstrator\Anaconda3\lib\site-packages\pandas\core\frame.py in set_index(self, keys, drop, append, inplace, verify_integrity)
2835 names.append(None)
2836 else:
-> 2837 level = frame[col]._values
2838 names.append(col)
2839 if drop:
C:\Users\Demonstrator\Anaconda3\lib\site-packages\pandas\core\frame.py in __getitem__(self, key)
1995 return self._getitem_multilevel(key)
1996 else:
-> 1997 return self._getitem_column(key)
1998
1999 def _getitem_column(self, key):
C:\Users\Demonstrator\Anaconda3\lib\site-packages\pandas\core\frame.py in _getitem_column(self, key)
2002 # get column
2003 if self.columns.is_unique:
-> 2004 return self._get_item_cache(key)
2005
2006 # duplicate columns & possible reduce dimensionality
C:\Users\Demonstrator\Anaconda3\lib\site-packages\pandas\core\generic.py in _get_item_cache(self, item)
1348 res = cache.get(item)
1349 if res is None:
-> 1350 values = self._data.get(item)
1351 res = self._box_item_values(item, values)
1352 cache[item] = res
C:\Users\Demonstrator\Anaconda3\lib\site-packages\pandas\core\internals.py in get(self, item, fastpath)
3288
3289 if not isnull(item):
-> 3290 loc = self.items.get_loc(item)
3291 else:
3292 indexer = np.arange(len(self.items))[isnull(self.items)]
C:\Users\Demonstrator\Anaconda3\lib\site-packages\pandas\indexes\base.py in get_loc(self, key, method, tolerance)
1945 return self._engine.get_loc(key)
1946 except KeyError:
-> 1947 return self._engine.get_loc(self._maybe_cast_indexer(key))
1948
1949 indexer = self.get_indexer([key], method=method, tolerance=tolerance)
pandas\index.pyx in pandas.index.IndexEngine.get_loc (pandas\index.c:4154)()
pandas\index.pyx in pandas.index.IndexEngine.get_loc (pandas\index.c:4018)()
pandas\hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_item (pandas\hashtable.c:12368)()
pandas\hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_item (pandas\hashtable.c:12322)()
KeyError: 'TIMESTAMP'
编辑
TypeError Traceback (most recent call last)
<ipython-input-890-86bff697504a> in <module>()
2 df2.resample('30min').mean()
3 fig, ax = plt.subplots()
----> 4 ax = sns.heatmap(df2.iloc[:, 1:6:], annot=True, linewidths=.5)
5 ax.set_yticklabels([i.strftime("%Y-%m-%d %H:%M:%S") for i in df2.index], rotation=0)
C:\Users\Demonstrator\Anaconda3\lib\site-packages\seaborn\matrix.py in heatmap(data, vmin, vmax, cmap, center, robust, annot, fmt, annot_kws, linewidths, linecolor, cbar, cbar_kws, cbar_ax, square, ax, xticklabels, yticklabels, mask, **kwargs)
483 plotter = _HeatMapper(data, vmin, vmax, cmap, center, robust, annot, fmt,
484 annot_kws, cbar, cbar_kws, xticklabels,
--> 485 yticklabels, mask)
486
487 # Add the pcolormesh kwargs here
C:\Users\Demonstrator\Anaconda3\lib\site-packages\seaborn\matrix.py in __init__(self, data, vmin, vmax, cmap, center, robust, annot, fmt, annot_kws, cbar, cbar_kws, xticklabels, yticklabels, mask)
165 # Determine good default values for the colormapping
166 self._determine_cmap_params(plot_data, vmin, vmax,
--> 167 cmap, center, robust)
168
169 # Sort out the annotations
C:\Users\Demonstrator\Anaconda3\lib\site-packages\seaborn\matrix.py in _determine_cmap_params(self, plot_data, vmin, vmax, cmap, center, robust)
202 cmap, center, robust):
203 """Use some heuristics to set good defaults for colorbar and range."""
--> 204 calc_data = plot_data.data[~np.isnan(plot_data.data)]
205 if vmin is None:
206 vmin = np.percentile(calc_data, 2) if robust else calc_data.min()
TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
推荐答案
You could alter the figsize by passing a tuple showing the width, height parameters you would like to keep.
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(10,10)) # Sample figsize in inches
sns.heatmap(df1.iloc[:, 1:6:], annot=True, linewidths=.5, ax=ax)
编辑
我记得您回答过类似的问题，您必须将索引设置为TIMESTAMP.因此，您可以执行以下操作:
I remember answering a similar question of yours where you had to set the index as TIMESTAMP. So, you could then do something like below:
df = df.set_index('TIMESTAMP')
df.resample('30min').mean()
fig, ax = plt.subplots()
ax = sns.heatmap(df.iloc[:, 1:6:], annot=True, linewidths=.5)
ax.set_yticklabels([i.strftime("%Y-%m-%d %H:%M:%S") for i in df.index], rotation=0)
对于您发布的数据框的head，图形如下所示:
For the head of the dataframe you posted, the plot would look like:
这篇关于使用Seaborn扩大热图的大小的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
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