I tried exploring everything in their website, but there is nothing regardless this ( https://plotly.com/python/v3/frequency-counts/ and https://plotly.com/python/v3/discrete-frequency/ won't solve my issue). I wanted to plot a graph just like seaborn countplot ( https://seaborn.pydata.org/generated/seaborn.countplot.html ).
I have this dataset:
id state value
19292 CA 100
24592 CA 200
12492 GE 340
11022 GE 500
99091 CO 250
59820 CO 220
50281 CA 900
I just wanted a barplot with CA, GE and CO in the x-axis and 3, 2 and 2 in the y-axis, respectively.
You only need to groupby
the state
first and then use count
just like so:
>>> import pandas as pd
>>> import matplotlib.pyplot as plt
>>> df
id state value
0 19292 CA 100
1 24592 CA 200
2 12492 GE 340
3 11022 GE 500
4 99091 CO 250
5 59820 CO 220
6 50281 CA 900
>>> new_df = df.groupby(["state"]).count().reset_index()
state id value
0 CA 3 3
1 CO 2 2
2 GE 2 2
>>> new_df.plot.bar(x="state", y="value")
>>> plt.show()
And it returns the following graph:
If you set plotly as your plotting backend for pandas , you can first group your data and do:
df.groupby(["state"]).count().reset_index().plot(x='state', y='value', kind='bar')
import pandas as pd
pd.options.plotting.backend = "plotly"
df = pd.DataFrame({'id': {0: 19292, 1: 24592, 2: 12492, 3: 11022, 4: 99091, 5: 59820, 6: 50281},
'state': {0: 'CA', 1: 'CA', 2: 'GE', 3: 'GE', 4: 'CO', 5: 'CO', 6: 'CA'},
'value': {0: 100, 1: 200, 2: 340, 3: 500, 4: 250, 5: 220, 6: 900}})
df.groupby(["state"]).count().reset_index().plot(x='state', y='value', kind='bar')
But if you would like a setup that you can expand a bit more on, I would use px.bar like so:
dfg = df.groupby(["state"]).count()
fig = px.bar(dfg, x=dfg.index, y="value")
fig.show()
The technical post webpages of this site follow the CC BY-SA 4.0 protocol. If you need to reprint, please indicate the site URL or the original address.Any question please contact:yoyou2525@163.com.