用海运把热图的尺寸放大

我创建了一个海运热图

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有很多数据时，热图就会太小，而且里面的数值开始不清晰，就像附加的图像一样。

我怎样才能改变热图的大小，使其更大？ 谢谢

剪辑

我试着:

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)

但我得到了这个错误:

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'

剪辑

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''