Display a Matrix, Including the Values, as a Heatmap

display a matrix, including the values, as a heatmap

Try heatmap.2 from the gplots package. The cellnote and notecol parameters control the text placed in cells. You'll probably want dendrogram = "none" as well.

Creating a Heatmap Matrix using two categorical values at axis and average of third column as value

You could create a pivot_table aggregating the mean of the weights. If needed, the heights and ages could be made categorical to fix a certain order.

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from io import StringIO

data_str = '''"age group" heightGroup weight
4-5 60-70   50
5-6 70-80   52
4-5 70-80   50
5-6 70-80   57
6-7 60-70   54
4-5 50-60   50
5-6 70-80   43'''
df = pd.read_csv(StringIO(data_str), delim_whitespace=True)
df_pivoted = df.pivot_table("weight", "age group", "heightGroup", aggfunc='mean')
ax = sns.heatmap(data=df_pivoted, annot=True, fmt='.1f')
plt.show()

PS: To mask out all cells with a count of 1 (or 0):

df_pivoted_count = df.pivot_table("weight", "age group", "heightGroup", aggfunc='count').fillna(0)
ax = sns.heatmap(data=df_pivoted, mask=df_pivoted_count <= 1, annot=True, fmt='.1f')

To show the heatmap with the counts for coloring: the counts dataframe (without .fillna()) can be used for data= and the means for annot=. The code below also changes the colorbar ticks to prevent that in this example, non-integer ticks would be shown.

df_pivoted = df.pivot_table("weight", "age group", "heightGroup", aggfunc='mean')
df_pivoted_count = df.pivot_table("weight", "age group", "heightGroup", aggfunc='count')
ax = sns.heatmap(data=df_pivoted_count, annot=df_pivoted, fmt='.1f', cmap='flare',
                 linecolor='skyblue', lw='2', clip_on=False, square=True,
                 cbar_kws={'ticks': range(1, int(df_pivoted_count.max().max()+1))})

Plotting a heatmap out of common value matrix in R

This should get you fairly close:

library(tidyverse)
df %>%
    rownames_to_column("row") %>%
    gather(col, Value, -row) %>%
    mutate(
        row = factor(row, levels = rev(unique(row))),
        Value = factor(Value)) %>%
    ggplot(aes(col, row, fill = Value)) +
    geom_tile() +
    scale_fill_manual(values = c(
        `3 cp` = "yellow",
        `4 cp` = "red",
        LOH = "blue",
        S3c = "lightgreen",
        UPD = "darkgreen",
        UPT = "black")) +
    labs(x = "", y = "") +
    theme_minimal()

Explanation:

1. Reshape data from wide to long.
2. Use geom_tile to draw a heatmap, where the fill colour is given by the factor levels of your values.
3. The rest is aesthetic "fluff" to increase the similarity to the image you link to.

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Sample data

df <- read.table(text =
    "        C1P C2P C3P C4P C5P
sam1    '3 cp'    '3 cp'    '3 cp'    '3 cp'    '3 cp'
sam2    'S3c' '4 cp'    '3 cp'    '3 cp'    'S3c'
sam3    '3 cp'    '3 cp'    '3 cp'    '3 cp'    '3 cp'
sam4    '3 cp'    '3 cp'    LOH LOH '3 cp'
sam5    '3 cp'    '3 cp'    '3 cp'    '3 cp'    '3 cp'
sam6    '4 cp'    '4 cp'    UPD UPD UPT", header = T)

show just the diagonal values of this heatmap

use the mask= parameter to choose which cells to mask (and therefore which to show).

You can use numpy's eye() function to quickly generate the mask

uniform_data = np.random.rand(5, 5)
diag = ~np.eye(*uniform_data.shape, dtype=bool)
ax = sns.heatmap(uniform_data, mask=diag)

Display values on heatmap in R

Is this what you mean? By providing the data object as the cellnote argument, the values are printed in the heatmap.

heatmap.2(data,           # cell labeling
          cellnote=data,
          notecex=1.0,
          notecol="cyan",
          na.color=par("bg"))

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