heatmap for large dataframe after grouping

I have a huge data frame that looks something like what i've attached below. I would like to group-by the column 'sscinames' as it has similar entries like 'Acidobacteria bacterium' (get the sum of it for each sample) and then plot a heatmap. Would like the heat map to only show the top 20 'sscinames' based on the sample count. Any help would be appreciated.

sscinames	S3_Day90_P3	S3_Day60_P3	S3_Day0_P1	S3_Day60_P1	S3_Day90_P1
    Thermoplasmata archaeon	4	0	41	1	5
    Planctomycetes bacterium	5	3	0	1	40
    Acidobacteria bacterium	6	15	0	8	13
    Trebonia kvetii	0	0	16	1	7
    Nonomuraea sp. RK-328	24	4	4	1	2
    Nitrospirae bacterium	3	1	4	1	2
    Acidobacteria bacterium	11	11	0	9	27

First aggregate sum and then for top N value by sum per aggregate rows use DataFrame.reindex with Series.nlargest:

Last for heatmap use seaborn.heatmap:

import seaborn as sns

N = 3
df1 = df.groupby('sscinames').sum()

out = df1.reindex(df1.sum(axis=1).nlargest(N).index)
print (out)
                          S3_Day90_P3  S3_Day60_P3  S3_Day0_P1  S3_Day60_P1  \
sscinames                                                                     
Acidobacteria bacterium            17           26           0           17   
Thermoplasmata archaeon             4            0          41            1   
Planctomycetes bacterium            5            3           0            1   

                          S3_Day90_P1  
sscinames                              
Acidobacteria bacterium            40  
Thermoplasmata archaeon             5  
Planctomycetes bacterium           40  


sns.heatmap(out, annot=True)

This requires a few steps, but it is a perfect task for pandas and seaborn. I commented the example below to give you an idea of what is happening there.

import pandas as pd
import seaborn as sns

# This is just to create a dataframe from your table, replace with importing yours
df1 = pd.DataFrame([["Thermoplasmata archaeon",  4,  0, 41, 1,  5],
                    ["Planctomycetes bacterium", 5,  3,  0, 1, 40],
                    ["Acidobacteria bacterium",  6, 15,  0, 8, 13],
                    ["Trebonia kvetii",          0,  0, 16, 1,  7],
                    ["Nonomuraea sp. RK-328",   24,  4,  4, 1,  2],
                    ["Nitrospirae bacterium",    3,  1,  4, 1,  2],
                    ["Acidobacteria bacterium", 11, 11,  0, 9, 27]])
df1.columns = ["sscinames", "S3_Day90_P3", "S3_Day60_P3",  "S3_Day0_P1", "S3_Day60_P1", "S3_Day90_P1"]

# Create column with total sample count
df2 = pd.DataFrame(df1.iloc[:,1:].T.sum()) # This sums (along rows, hence the .T) all samples for each row
df2.columns = ["Total Samples"]
# ...and merge with your data frame to add the new column (axis=1)
df = pd.concat([df1, df2], axis=1)

# Now, turn the first column into a pandas index (then seaborn uses it immediately to label the axes)
df = df.set_index(['sscinames'])

# You can now sort your dataframe by the new column and proceed with the top entries
df = df.sort_values(by="Total Samples", ascending=False)
df_slice2plot = df.iloc[:5, :] # This takes the top five rows and all the columns, change as needed

# Now, on to plotting
sns.heatmap(df_slice2plot.iloc[:, 1:]) # Plot numeric columns (first column, with index 0, are the labels)

This is the result I get:

A heatmap of the example data