How to hide/remove labels with No Data in a multiple labeled horizontal Bar Chart-CodePudding

Following this question: Bar Chart with multiple labels. I tried to replicate the code with a horizontal bar chart. I would like to know how I could remove/hide the labels with No data.

So instead of this: Graph with No Data labels

looks more like this: Graph only showing the labels with Data

I attach the code below.

I would appreciate it if someone could steer me in the right direction here. Thank you in advance.

Cheers

import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import ticker
import numpy as np

df = pd.DataFrame(
    {"DEPARTMENT": ["Accounting", "Administration", "Finance", "Finance", "Human resources", "IT", "Marketing", "R&D"], "DATE": ["June", "June", "July", "June", "June","June","June","June"], "COUNT": [10, 33, 16, 23, 3, 3, 5, 9]}
)

test=pd.pivot_table(df, values='COUNT', index='DATE',columns='DEPARTMENT',
            aggfunc=np.sum)

fig=plt.figure(dpi=300)
ax=test.plot(kind='barh',color=['#4472C4'], legend=False)
ax = plt.gca()
pos = []
for bar in ax.patches:
    pos.append(bar.get_y() bar.get_height()/2.)

ax.set_yticks(pos,minor=True)
lab = []
for i in range(len(pos)):
    l = test.columns.values[i//len(test.index.values)]
    lab.append(l)

ax.set_yticklabels(lab,minor=True)
ax.tick_params(axis='y', which='major', pad=120, size=0)
plt.setp(ax.get_xticklabels(), rotation=0)

plt.show()
plt.close()

CodePudding user response：

I edited your code to check if each cell value is nan when creating the labels.

If the cell value is nan then the label value is set to an empty string, if the cell value is not nan then the true label is used.

import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import ticker
import numpy as np

df = pd.DataFrame(
    {"DEPARTMENT": ["Accounting", "Administration", "Finance", "Finance", "Human resources", "IT", "Marketing", "R&D"], "DATE": ["June", "June", "July", "June", "June","June","June","June"], "COUNT": [10, 33, 16, 23, 3, 3, 5, 9]}
)

test=pd.pivot_table(df, values='COUNT', index='DATE',columns='DEPARTMENT',
            aggfunc=np.sum)

fig=plt.figure(dpi=300)
ax=test.plot(kind='barh',color=['#4472C4'], legend=False)
ax = plt.gca()
pos = []
for bar in ax.patches:
    pos.append(bar.get_y() bar.get_height()/2.)

ax.set_yticks(pos,minor=True)
lab = []

#EDITED FROM HERE
arr = []
for i in range(len(test.iloc[0].values)):
    arr.append(test.iloc[0].values[i])
    arr.append(test.iloc[1].values[i])

for i in range(len(pos)):
    l = test.columns.values[i//len(test.index.values)]
    val = arr[i]
    if np.isnan(val):
        lab.append("")
    else: 
        lab.append(l)
#TO HERE

ax.set_yticklabels(lab,minor=True)
ax.tick_params(axis='y', which='major', pad=120, size=0)
plt.setp(ax.get_xticklabels(), rotation=0)

plt.show()
plt.close()