I have table in xls that I need to visulaise in chart. Im using Python Pandas. Table has this structure:
ORP | Produkce_obyv._kg |
---|---|
a | 289,77 |
a | 333,31 |
... | ... |
b | 198,69 |
b | 214,71 |
... | ... |
Im using this code:
group1 = data[data['ORP']=='A']
group2 = data[data['ORP']=='B']
graf = group1.groupby('ORP')['Produkce_obyv._kg'].mean().to_frame(name='A')
graf['B'] = group2.groupby('ORP')['Produkce_obyv._kg'].mean()
graf.plot.bar()
plt.show()
Chart prints only one bar (A):
I need to see data (B) as second bar. I am using them for statistcs:
ttest_ind_result = ttest_ind(group1['Produkce_obyv._kg'], group2['Produkce_obyv._kg'], equal_var=False)
print(ttest_ind_result)
That works fine. Where is problem with chart? Thanks!
I tried to use different library, tried to use analysis
print(graf.isna().sum())
and
print(len(group2))
print(group2['ORP'].unique())
All seems to be correct.
Okay, taking the data in the question, you could plot this with matplotlib
quite quickly.
The main difference is the use of df.groupby('ORP').mean()
as you will see below.
here is an example:
import pandas as pd
import matplotlib.pyplot as plt
# example data
data = {'ORP': ['a', 'a', 'a', 'a', 'a', 'a', 'b', 'b', 'b', 'b'],
'Produkce_obyv._kg': [289.77, 333.31, 309.42, 271.53, 367.32, 351.37, 198.69, 214.71, 244.35, 224.15]}
df = pd.DataFrame(data)
# do the plot
grouped = df.groupby('ORP').mean()
grouped.plot(kind='bar')
plt.xlabel('ORP')
plt.ylabel('Produkce_obyv._kg')
plt.title('Average Produkce_obyv._kg by ORP')
plt.show()
and this is the result: