I have a dataframe with this data and want to plot it with a bar graph with x-axis labels being months
import pandas as pd
data = {'Birthday': ['1900-01-31', '1900-02-28', '1900-03-31', '1900-04-30', '1900-05-31', '1900-06-30', '1900-07-31', '1900-08-31', '1900-09-30', '1900-10-31', '1900-11-30', '1900-12-31'],
'Players': [32, 25, 27, 19, 27, 18, 18, 21, 23, 21, 26, 23]}
df = pd.DataFrame(data)
Birthday Players
1900-01-31 32
1900-02-28 25
1900-03-31 27
1900-04-30 19
1900-05-31 27
1900-06-30 18
1900-07-31 18
1900-08-31 21
1900-09-30 23
1900-10-31 21
1900-11-30 26
1900-12-31 23
This is what I have
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
fig = plt.figure(figsize=(12, 7))
locator = mdates.MonthLocator()
fmt = mdates.DateFormatter('%b')
X = plt.gca().xaxis
X.set_major_locator(locator)
X.set_major_formatter(fmt)
plt.bar(month_df.index, month_df.Players, color = 'maroon', width=10)
but the result is this with the label starting from Feb instead of Jan
CodePudding user response:
I'm not familiar with matplotlib.dates but because you are using pandas there are simple ways doing what you need using pandas.
Here is my code:
import pandas as pd
import calendar
from matplotlib import pyplot as plt
# read data
month_df = pd.read_csv('data.csv', delim_whitespace=True)
# convert column to datetime
month_df["Birthday"] = pd.to_datetime(month_df["Birthday"], format="%Y-%m-%d")
# group by month and plot a bar plot
month_df.groupby(month_df["Birthday"].dt.month).sum().plot(kind="bar", color = "maroon")
# set plot properties
plt.ylabel("Count")
plt.xlabel("Birthday Month")
plt.xticks(ticks = range(0,12) ,labels = calendar.month_name[1:])
# show plot
plt.show()
Output:
CodePudding user response:
Typically, matplotlib.bar does not do a very good job with datetimes for various reasons. It's easy to manually set your x tick locations and labels as below. This a