How do you plot the bars of a bar plot different colors only using the pandas dataframe plot method?
If I have this DataFrame:
df = pd.DataFrame({'count': {0: 3372, 1: 68855, 2: 17948, 3: 708, 4: 9117}}).reset_index() index count 0 0 3372 1 1 68855 2 2 17948 3 3 708 4 4 9117
What df.plot() arguments do I need to set so each bar in the plot:
- Uses the 'Paired' colormap
- Plots each bar a different color
What I am attempting:
df.plot(x='index', y='count', kind='bar', label='index', colormap='Paired', use_index=False)
The result:
What I already know (yes, this works, but again, my purpose is to figure out how to do this with df.plot ONLY. Surely it must be possible?):
def f(df): groups = df.groupby('index') for name,group in groups: plt.bar(name, group['count'], label=name, align='center') plt.legend() plt.show()
There is no argument you can pass to df.plot that colorizes the bars differently for a single column.
Since bars for different columns are colorized differently, an option is to transpose the dataframe before plotting,
ax = df.T.plot(kind='bar', label='index', colormap='Paired')
This would now draw the data as part of a subgroup. Therefore some tweaking needs to be applied to set the limits and xlabels correctly.
import matplotlib.pyplot as plt import pandas as pd df = pd.DataFrame({'count': {0: 3372, 1: 68855, 2: 17948, 3: 708, 4: 9117}}).reset_index() ax = df.T.plot(kind='bar', label='index', colormap='Paired') ax.set_xlim(0.5, 1.5) ax.set_xticks([0.8,0.9,1,1.1,1.2]) ax.set_xticklabels(range(len(df))) plt.show()
While I guess this solution matches the criteria from the question, there is actually nothing wrong with using plt.bar. A single call to plt.bar is sufficient
plt.bar(range(len(df)), df["count"], color=plt.cm.Paired(np.arange(len(df))))
Complete code:
import matplotlib.pyplot as plt import pandas as pd import numpy as np df = pd.DataFrame({'count': {0: 3372, 1: 68855, 2: 17948, 3: 708, 4: 9117}}).reset_index() plt.bar(range(len(df)), df["count"], color=plt.cm.Paired(np.arange(len(df)))) plt.show()
