Wsrtjtyk

I have a dataframe,df, with 29 rows and 92 columns with the rows being datetime Index and float as the column names representing the frequency of each of the column names day by day. I intend to plot a heatmap but i am getting a scattered plot. An example of dataframe looks like:

       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

This is heatmap function that was called:

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

So running the function with its argument, I used:

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()

However i get this :