Plotting a stacked bar chart with matplotlib using dates as index where there are gaps and missing data

Question

I have a data frame containing several columns for which I have continuous (annual) data since 1971 up to 2012. After that I have some say "predicted" values for 2020, 2025, 2030 and 2035. The index to the data frame is in integer format (each date), and I've tried converting it to a date time format using the appropriate module, but this still doesn't correctly space out the dates on the x-axis (to show the actual time-gaps) Here's the code I've been experimenting with:

fig, ax = plt.subplots()
# Set title
ttl = "India's fuel mix (1971-2012)"

# Set color transparency (0: transparent; 1: solid)
a = 0.7


# Convert the index integer dates into actual date objects
new_fmt.index = [datetime.datetime(year=date, month=1, day=1) for date in new_fmt.index]


new_fmt.ix[:,['Coal', 'Oil', 'Gas', 'Biofuels', 'Nuclear',    'Hydro','Wind']].plot(ax=ax,kind='bar', stacked=True, title = ttl)
ax.grid(False)
xlab = 'Date (Fiscal Year)'
ylab = 'Electricity Generation (GWh)'
ax.set_title(ax.get_title(), fontsize=20, alpha=a)
ax.set_xlabel(xlab, fontsize=16, alpha=a)
ax.set_ylabel(ylab, fontsize=16, alpha=a)

# Tell matplotlib to interpret the x-axis values as dates
ax.xaxis_date()

# Make space for and rotate the x-axis tick labels
fig.autofmt_xdate()

Sara · Accepted Answer · 2014-11-13 17:46:56Z

1

I tried to figure it out:

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import datetime

# create data frame with random data (3 rows, 2 columns)
df = pd.DataFrame(np.random.randn(3,2))
# time index with missing years
t = [datetime.date(year=1971, month=12, day=31), datetime.date(year=1972, month=12, day=31), datetime.date(year=1980, month=12, day=31)]
df.index = t

# time index with all the years:
tnew = pd.date_range(datetime.date(year=1971, month=1, day=1),datetime.date(year=1981, month=1, day=1),freq="A")

# reindex data frame (missing years will be filled with NaN
df2 = df.reindex(tnew)

# replace NaN with 0
df2_zeros = df2.fillna(0)

# or interpolate
df2_interp = df2.interpolate()

# and plot
df2_interp.columns = ["coal","wind"]
df2_interp.plot(kind='bar', stacked=True)
plt.show()

Hope this helps.

edited Nov 13, 2014 at 17:46

Sara

9832 gold badges11 silver badges16 bronze badges

answered Oct 15, 2014 at 17:07

Johannes Lötscher

763 bronze badges

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