Annotate heatmap with value from Pandas dataframe
Question:
I would like to annotate a heatmap with the values that I pass from a dataframe into the function below. I have looked at matplotlib.text but have not been able to get the values from my dataframe in a desired way in my heatmap. I have pasted in my function for generating a heatmap below, after that my dataframe and the output from the heatmap call. I would like to plot each value from my dataframe in the center of each cell in the heatmap.
Function for generating a heatmap:
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
def heatmap_binary(df,
edgecolors='w',
#cmap=mpl.cm.RdYlGn,
log=False):
width = len(df.columns)/7*10
height = len(df.index)/7*10
fig, ax = plt.subplots(figsize=(20,10))#(figsize=(width,height))
cmap, norm = mcolors.from_levels_and_colors([0, 0.05, 1],['Teal', 'MidnightBlue'] ) # ['MidnightBlue', Teal]['Darkgreen', 'Darkred']
heatmap = ax.pcolor(df ,
edgecolors=edgecolors, # put white lines between squares in heatmap
cmap=cmap,
norm=norm)
ax.autoscale(tight=True) # get rid of whitespace in margins of heatmap
ax.set_aspect('equal') # ensure heatmap cells are square
ax.xaxis.set_ticks_position('top') # put column labels at the top
ax.tick_params(bottom='off', top='off', left='off', right='off') # turn off ticks
plt.yticks(np.arange(len(df.index)) + 0.5, df.index, size=20)
plt.xticks(np.arange(len(df.columns)) + 0.5, df.columns, rotation=90, size= 15)
# ugliness from http://matplotlib.org/users/tight_layout_guide.html
from mpl_toolkits.axes_grid1 import make_axes_locatable
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "3%", pad="1%")
plt.colorbar(heatmap, cax=cax)
plt.show()
Herre is an example of My dataframe :
dataframe :
0-5 km / h 5-40 km / h 40-80 km / h 80-120 km / h
NORDIC 0.113955 0.191888 0.017485 -0.277528
MIDDLE EU 0.117903 0.197084 -0.001447 -0.332677
KOREA 0.314008 0.236503 -0.067174 -0.396518
CHINA 0.314008 0.236503 -0.067174 -0.396518
120-160 km / h 160-190 km / h 190 km / h
NORDIC -0.054365 0.006107 0.002458
MIDDLE EU 0.002441 0.012097 0.004599
KOREA -0.087191 0.000331 0.000040
CHINA -0.087191 0.000331 0.000040
Generating the heatmap:
heatmap_binary(dataframe)
Any ideas?
Update to clarify my problem
I tried the proposed solution from question which has the result I’m looking for:
how to annotate heatmap with text in matplotlib?
However, I still have a problem using the matplotlib.text function for positioning the values in the heatmap:
Here is my cod for trying this solution:
import matplotlib.pyplot as plt
import numpy as np
data = dataframe.values
heatmap_binary(dataframe)
for y in range(data.shape[0]):
for x in range(data.shape[1]):
plt.text(data[y,x] +0.05 , data[y,x] + 0.05, '%.4f' % data[y, x], #data[y,x] +0.05 , data[y,x] + 0.05
horizontalalignment='center',
verticalalignment='center',
color='w')
#plt.colorbar(heatmap)
plt.show()
added plot: (different coloring but same problem)
Answers:
This is because you’re using plt.text after you’ve added another axes.
The state machine will plot on the current axes, and after you’ve added a new one with divider.append_axes, the colorbar’s axes is the current one. (Just calling plt.colorbar will not cause this, as it sets the current axes back to the original one afterwards if it creates the axes itself. If a specific axes object is passed in using the cax kwarg, it doesn’t reset the “current” axes, as that’s not what you’d normally want.)
Things like this are the main reason that you’ll see so many people advising that you use the OO interface to matplotlib instead of the state machine interface. That way you know which axes object that you’re plotting on.
For example, in your case, you could have heatmap_binary return the ax object that it creates, and the plot using ax.text instead of plt.text (and similar for the other plotting methods).
The values you were using for your coordinates in your for loop were screwed up. Also you were using plt.colorbar instead of something cleaner like fig.colorbar. Try this (it gets the job done, with no effort made to otherwise cleanup the code):
def heatmap_binary(df,
edgecolors='w',
#cmap=mpl.cm.RdYlGn,
log=False):
width = len(df.columns)/7*10
height = len(df.index)/7*10
fig, ax = plt.subplots(figsize=(20,10))#(figsize=(width,height))
cmap, norm = mcolors.from_levels_and_colors([0, 0.05, 1],['Teal', 'MidnightBlue'] ) # ['MidnightBlue', Teal]['Darkgreen', 'Darkred']
heatmap = ax.pcolor(df ,
edgecolors=edgecolors, # put white lines between squares in heatmap
cmap=cmap,
norm=norm)
data = df.values
for y in range(data.shape[0]):
for x in range(data.shape[1]):
plt.text(x + 0.5 , y + 0.5, '%.4f' % data[y, x], #data[y,x] +0.05 , data[y,x] + 0.05
horizontalalignment='center',
verticalalignment='center',
color='w')
ax.autoscale(tight=True) # get rid of whitespace in margins of heatmap
ax.set_aspect('equal') # ensure heatmap cells are square
ax.xaxis.set_ticks_position('top') # put column labels at the top
ax.tick_params(bottom='off', top='off', left='off', right='off') # turn off ticks
ax.set_yticks(np.arange(len(df.index)) + 0.5)
ax.set_yticklabels(df.index, size=20)
ax.set_xticks(np.arange(len(df.columns)) + 0.5)
ax.set_xticklabels(df.columns, rotation=90, size= 15)
# ugliness from http://matplotlib.org/users/tight_layout_guide.html
from mpl_toolkits.axes_grid1 import make_axes_locatable
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "3%", pad="1%")
fig.colorbar(heatmap, cax=cax)
Then
df1 = pd.DataFrame(np.random.choice([0, 0.75], size=(4,5)), columns=list('ABCDE'), index=list('WXYZ'))
heatmap_binary(df1)
gives:
This functionality is provided by the seaborn package. It can produce maps like
An example usage of seaborn is
import seaborn as sns
sns.set()
# Load the example flights dataset and conver to long-form
flights_long = sns.load_dataset("flights")
flights = flights_long.pivot("month", "year", "passengers")
# Draw a heatmap with the numeric values in each cell
sns.heatmap(flights, annot=True, fmt="d", linewidths=.5)
You also can use plotly.figure_factory to create heatmap from DataFrame, but you have convert it into list.
import plotly.figure_factory as ff
z = [your_dataframe].values.tolist()
x = [your_dataframe].columns.tolist()
y = [your_dataframe].index.tolist()
fig = ff.create_annotated_heatmap(z, x=x, y=y, annotation_text=z, colorscale='viridis')
# for add annotation into Heatmap
for i in range(len(fig.layout.annotations)):
fig.layout.annotations[i].font.size = 12
# show your Heatmap
fig.show()
Check this link from plotly, It shows you all cases that you may need to show your heatmaps.
I would like to annotate a heatmap with the values that I pass from a dataframe into the function below. I have looked at matplotlib.text but have not been able to get the values from my dataframe in a desired way in my heatmap. I have pasted in my function for generating a heatmap below, after that my dataframe and the output from the heatmap call. I would like to plot each value from my dataframe in the center of each cell in the heatmap.
Function for generating a heatmap:
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
def heatmap_binary(df,
edgecolors='w',
#cmap=mpl.cm.RdYlGn,
log=False):
width = len(df.columns)/7*10
height = len(df.index)/7*10
fig, ax = plt.subplots(figsize=(20,10))#(figsize=(width,height))
cmap, norm = mcolors.from_levels_and_colors([0, 0.05, 1],['Teal', 'MidnightBlue'] ) # ['MidnightBlue', Teal]['Darkgreen', 'Darkred']
heatmap = ax.pcolor(df ,
edgecolors=edgecolors, # put white lines between squares in heatmap
cmap=cmap,
norm=norm)
ax.autoscale(tight=True) # get rid of whitespace in margins of heatmap
ax.set_aspect('equal') # ensure heatmap cells are square
ax.xaxis.set_ticks_position('top') # put column labels at the top
ax.tick_params(bottom='off', top='off', left='off', right='off') # turn off ticks
plt.yticks(np.arange(len(df.index)) + 0.5, df.index, size=20)
plt.xticks(np.arange(len(df.columns)) + 0.5, df.columns, rotation=90, size= 15)
# ugliness from http://matplotlib.org/users/tight_layout_guide.html
from mpl_toolkits.axes_grid1 import make_axes_locatable
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "3%", pad="1%")
plt.colorbar(heatmap, cax=cax)
plt.show()
Herre is an example of My dataframe :
dataframe :
0-5 km / h 5-40 km / h 40-80 km / h 80-120 km / h
NORDIC 0.113955 0.191888 0.017485 -0.277528
MIDDLE EU 0.117903 0.197084 -0.001447 -0.332677
KOREA 0.314008 0.236503 -0.067174 -0.396518
CHINA 0.314008 0.236503 -0.067174 -0.396518
120-160 km / h 160-190 km / h 190 km / h
NORDIC -0.054365 0.006107 0.002458
MIDDLE EU 0.002441 0.012097 0.004599
KOREA -0.087191 0.000331 0.000040
CHINA -0.087191 0.000331 0.000040
Generating the heatmap:
heatmap_binary(dataframe)
Any ideas?
Update to clarify my problem
I tried the proposed solution from question which has the result I’m looking for:
how to annotate heatmap with text in matplotlib?
However, I still have a problem using the matplotlib.text function for positioning the values in the heatmap:
Here is my cod for trying this solution:
import matplotlib.pyplot as plt
import numpy as np
data = dataframe.values
heatmap_binary(dataframe)
for y in range(data.shape[0]):
for x in range(data.shape[1]):
plt.text(data[y,x] +0.05 , data[y,x] + 0.05, '%.4f' % data[y, x], #data[y,x] +0.05 , data[y,x] + 0.05
horizontalalignment='center',
verticalalignment='center',
color='w')
#plt.colorbar(heatmap)
plt.show()
added plot: (different coloring but same problem)
This is because you’re using plt.text after you’ve added another axes.
The state machine will plot on the current axes, and after you’ve added a new one with divider.append_axes, the colorbar’s axes is the current one. (Just calling plt.colorbar will not cause this, as it sets the current axes back to the original one afterwards if it creates the axes itself. If a specific axes object is passed in using the cax kwarg, it doesn’t reset the “current” axes, as that’s not what you’d normally want.)
Things like this are the main reason that you’ll see so many people advising that you use the OO interface to matplotlib instead of the state machine interface. That way you know which axes object that you’re plotting on.
For example, in your case, you could have heatmap_binary return the ax object that it creates, and the plot using ax.text instead of plt.text (and similar for the other plotting methods).
The values you were using for your coordinates in your for loop were screwed up. Also you were using plt.colorbar instead of something cleaner like fig.colorbar. Try this (it gets the job done, with no effort made to otherwise cleanup the code):
def heatmap_binary(df,
edgecolors='w',
#cmap=mpl.cm.RdYlGn,
log=False):
width = len(df.columns)/7*10
height = len(df.index)/7*10
fig, ax = plt.subplots(figsize=(20,10))#(figsize=(width,height))
cmap, norm = mcolors.from_levels_and_colors([0, 0.05, 1],['Teal', 'MidnightBlue'] ) # ['MidnightBlue', Teal]['Darkgreen', 'Darkred']
heatmap = ax.pcolor(df ,
edgecolors=edgecolors, # put white lines between squares in heatmap
cmap=cmap,
norm=norm)
data = df.values
for y in range(data.shape[0]):
for x in range(data.shape[1]):
plt.text(x + 0.5 , y + 0.5, '%.4f' % data[y, x], #data[y,x] +0.05 , data[y,x] + 0.05
horizontalalignment='center',
verticalalignment='center',
color='w')
ax.autoscale(tight=True) # get rid of whitespace in margins of heatmap
ax.set_aspect('equal') # ensure heatmap cells are square
ax.xaxis.set_ticks_position('top') # put column labels at the top
ax.tick_params(bottom='off', top='off', left='off', right='off') # turn off ticks
ax.set_yticks(np.arange(len(df.index)) + 0.5)
ax.set_yticklabels(df.index, size=20)
ax.set_xticks(np.arange(len(df.columns)) + 0.5)
ax.set_xticklabels(df.columns, rotation=90, size= 15)
# ugliness from http://matplotlib.org/users/tight_layout_guide.html
from mpl_toolkits.axes_grid1 import make_axes_locatable
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "3%", pad="1%")
fig.colorbar(heatmap, cax=cax)
Then
df1 = pd.DataFrame(np.random.choice([0, 0.75], size=(4,5)), columns=list('ABCDE'), index=list('WXYZ'))
heatmap_binary(df1)
gives:
This functionality is provided by the seaborn package. It can produce maps like
An example usage of seaborn is
import seaborn as sns
sns.set()
# Load the example flights dataset and conver to long-form
flights_long = sns.load_dataset("flights")
flights = flights_long.pivot("month", "year", "passengers")
# Draw a heatmap with the numeric values in each cell
sns.heatmap(flights, annot=True, fmt="d", linewidths=.5)
You also can use plotly.figure_factory to create heatmap from DataFrame, but you have convert it into list.
import plotly.figure_factory as ff
z = [your_dataframe].values.tolist()
x = [your_dataframe].columns.tolist()
y = [your_dataframe].index.tolist()
fig = ff.create_annotated_heatmap(z, x=x, y=y, annotation_text=z, colorscale='viridis')
# for add annotation into Heatmap
for i in range(len(fig.layout.annotations)):
fig.layout.annotations[i].font.size = 12
# show your Heatmap
fig.show()
Check this link from plotly, It shows you all cases that you may need to show your heatmaps.