绘制时混乱的热图

Question

我有一个数据帧df，有29行和92列，其中的行是datetime索引，并且float作为列名，表示每一列名称的频率。我打算绘制一个热图，但得到的是散布图。数据框的示例如下：

       Index                        0.0     1.0     2.0..... 91
      2017-08-03 00:00:00           10        0      10       0
      2017-08-04 00:00:00           20       60    1470      20
      2017-08-05 00:00:00           0        58       0      24
      2017-08-06 00:00:00           0         0     480      24
      2017-09-07 00:00:00           0         0       0      25
            :                       :         :       :      :
            :                       :         :       :      :
      2017-09-30 00:00:00

这是被称为的热图函数：

def heatmap(data, row_labels, col_labels, ax=None,
        cbar_kw={}, cbarlabel="", **kwargs):
"""
Create a heatmap from a numpy array and two lists of labels.

Arguments:
    data       : A 2D numpy array of shape (N,M)
    row_labels : A list or array of length N with the labels
                 for the rows
    col_labels : A list or array of length M with the labels
                 for the columns
Optional arguments:
    ax         : A matplotlib.axes.Axes instance to which the heatmap
                 is plotted. If not provided, use current axes or
                 create a new one.
    cbar_kw    : A dictionary with arguments to
                 :meth:`matplotlib.Figure.colorbar`.
    cbarlabel  : The label for the colorbar
All other arguments are directly passed on to the imshow call.
"""

if not ax:
    ax = plt.gca()

# Plot the heatmap
im = ax.imshow(data, **kwargs)

# Create colorbar
cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)
cbar.ax.set_ylabel(cbarlabel, rotation=-90, va="bottom")

# We want to show all ticks...
ax.set_xticks(np.arange(data.shape[1]))
ax.set_yticks(np.arange(data.shape[0]))
# ... and label them with the respective list entries.
ax.set_xticklabels(col_labels)
ax.set_yticklabels(row_labels)

# Let the horizontal axes labeling appear on top.
ax.tick_params(top=True, bottom=False,
               labeltop=True, labelbottom=False)

# Rotate the tick labels and set their alignment.
plt.setp(ax.get_xticklabels(), rotation=-30, ha="right",
         rotation_mode="anchor")

# Turn spines off and create white grid.
for edge, spine in ax.spines.items():
    spine.set_visible(False)

ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)
ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)
ax.grid(which="minor", color="w", linestyle='-', linewidth=3)
ax.tick_params(which="minor", bottom=False, left=False)

return im, cbar


def annotate_heatmap(im, data=None, valfmt="{x:.2f}",
                 textcolors=["black", "white"],
                 threshold=None, **textkw):
"""
A function to annotate a heatmap.

Arguments:
    im         : The AxesImage to be labeled.
Optional arguments:
    data       : Data used to annotate. If None, the image's data is used.
    valfmt     : The format of the annotations inside the heatmap.
                 This should either use the string format method, e.g.
                 "$ {x:.2f}", or be a :class:`matplotlib.ticker.Formatter`.
    textcolors : A list or array of two color specifications. The first is
                 used for values below a threshold, the second for those
                 above.
    threshold  : Value in data units according to which the colors from
                 textcolors are applied. If None (the default) uses the
                 middle of the colormap as separation.

Further arguments are passed on to the created text labels.
"""

if not isinstance(data, (list, np.ndarray)):
    data = im.get_array()

# Normalize the threshold to the images color range.
if threshold is not None:
    threshold = im.norm(threshold)
else:
    threshold = im.norm(data.max())/2.

# Set default alignment to center, but allow it to be
# overwritten by textkw.
kw = dict(horizontalalignment="center",
          verticalalignment="center")
kw.update(textkw)

# Get the formatter in case a string is supplied
if isinstance(valfmt, str):
    valfmt = matplotlib.ticker.StrMethodFormatter(valfmt)

# Loop over the data and create a `Text` for each "pixel".
# Change the text's color depending on the data.
texts = []
for i in range(data.shape[0]):
    for j in range(data.shape[1]):
        kw.update(color=textcolors[im.norm(data[i, j]) > threshold])
        text = im.axes.text(j, i, valfmt(data[i, j], None), **kw)
        texts.append(text)

return texts

因此，我使用参数运行该函数：

Rownames=list(df.index.values)
ColumnNames=list(df.columns.values)
fig, ax = plt.subplots()

im, cbar = heatmap(df, Rownames, ColumnNames, ax=ax,
               cmap="YlGn", cbarlabel="frequency [freq/day]")
texts = annotate_heatmap(im, valfmt="{x:.1f} ")

fig.tight_layout()
plt.show()

但是我得到了：