matplotlib: how to prevent x-axis labels from overlapping

I'm generating a bar-chart with matplotlib. It all works well but I can't figure out how to prevent the labels of the x-axis from overlapping each other. Here an example:

Here is some sample SQL for a postgres 9.1 database:

drop table if exists mytable; create table mytable(id bigint, version smallint, date_from timestamp without time zone); insert into mytable(id, version, date_from) values ('4084036', '1', '2006-12-22 22:46:35'), ('4084938', '1', '2006-12-23 16:19:13'), ('4084938', '2', '2006-12-23 16:20:23'), ('4084939', '1', '2006-12-23 16:29:14'), ('4084954', '1', '2006-12-23 16:28:28'), ('4250653', '1', '2007-02-12 21:58:53'), ('4250657', '1', '2007-03-12 21:58:53') ; 

And this is my python-script:

# -*- coding: utf-8 -*- #!/usr/bin/python2.7 import psycopg2 import matplotlib.pyplot as plt fig = plt.figure() # for savefig() import pylab ### ### Connect to database with psycopg2 ### try: conn_string="dbname='x' user='y' host='z' password='pw'" print "Connecting to database\n->%s" % (conn_string) conn = psycopg2.connect(conn_string) print "Connection to database was established succesfully" except: print "Connection to database failed" ### ### Execute SQL query ### # New cursor method for sql cur = conn.cursor() # Execute SQL query. For more than one row use three '"' try: cur.execute(""" -- In which year/month have these points been created? -- Need 'yyyymm' because I only need Months with years (values are summeed up). Without, query returns every day the db has an entry. SELECT to_char(s.day,'yyyymm') AS month ,count(t.id)::int AS count FROM ( SELECT generate_series(min(date_from)::date ,max(date_from)::date ,interval '1 day' )::date AS day FROM mytable t ) s LEFT JOIN mytable t ON t.date_from::date = s.day GROUP BY month ORDER BY month; """) # Return the results of the query. Fetchall() = all rows, fetchone() = first row records = cur.fetchall() cur.close() except: print "Query could not be executed" # Unzip the data from the db-query. Order is the same as db-query output year, count = zip(*records) ### ### Plot (Barchart) ### # Count the length of the range of the count-values, y-axis-values, position of axis-labels, legend-label plt.bar(range(len(count)), count, align='center', label='Amount of created/edited points') # Add database-values to the plot with an offset of 10px/10px ax = fig.add_subplot(111) for i,j in zip(year,count): ax.annotate(str(j), xy=(i,j), xytext=(10,10), textcoords='offset points') # Rotate x-labels on the x-axis fig.autofmt_xdate() # Label-values for x and y axis plt.xticks(range(len(count)), (year)) # Label x and y axis plt.xlabel('Year') plt.ylabel('Amount of created/edited points') # Locate legend on the plot () plt.legend(loc=1) # Plot-title plt.title("Amount of created/edited points over time") # show plot pylab.show() 

Is there a way how I can prevent the labels from overlapping each other? Ideally in an automatic way, because I can't predict the amount of bars.

4 Answers

I think you're confused on a few points about how matplotlib handles dates.

You're not actually plotting dates, at the moment. You're plotting things on the x-axis with [0,1,2,...] and then manually labeling every point with a string representation of the date.

Matplotlib will automatically position ticks. However, you're over-riding matplotlib's tick positioning functionality (Using xticks is basically saying: "I want ticks in exactly these positions".)

At the moment, you'll get ticks at [10, 20, 30, ...] if matplotlib automatically positions them. However, these will correspond to the values that you used to plot them, not the dates (which you didn't use when plotting).

You probably want to actually plot things using dates.

Currently, you're doing something like this:

import datetime as dt import matplotlib.dates as mdates import numpy as np import matplotlib.pyplot as plt # Generate a series of dates (these are in matplotlib's internal date format) dates = mdates.drange(dt.datetime(2010, 01, 01), dt.datetime(2012,11,01), dt.timedelta(weeks=3)) # Create some data for the y-axis counts = np.sin(np.linspace(0, np.pi, dates.size)) # Set up the axes and figure fig, ax = plt.subplots() # Make a bar plot, ignoring the date values ax.bar(np.arange(counts.size), counts, align='center', width=1.0) # Force matplotlib to place a tick at every bar and label them with the date datelabels = mdates.num2date(dates) # Go back to a sequence of datetimes... ax.set(xticks=np.arange(dates.size), xticklabels=datelabels) #Same as plt.xticks # Make space for and rotate the x-axis tick labels fig.autofmt_xdate() plt.show() 

Instead, try something like this:

import datetime as dt import matplotlib.dates as mdates import numpy as np import matplotlib.pyplot as plt # Generate a series of dates (these are in matplotlib's internal date format) dates = mdates.drange(dt.datetime(2010, 01, 01), dt.datetime(2012,11,01), dt.timedelta(weeks=3)) # Create some data for the y-axis counts = np.sin(np.linspace(0, np.pi, dates.size)) # Set up the axes and figure fig, ax = plt.subplots() # By default, the bars will have a width of 0.8 (days, in this case) We want # them quite a bit wider, so we'll make them them the minimum spacing between # the dates. (To use the exact code below, you'll need to convert your sequence # of datetimes into matplotlib's float-based date format. # Use "dates = mdates.date2num(dates)" to convert them.) width = np.diff(dates).min() # Make a bar plot. Note that I'm using "dates" directly instead of plotting # "counts" against x-values of [0,1,2...] ax.bar(dates, counts, align='center', width=width) # 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() plt.show() 

As for your question on how to show only every 4th tick (for example) on the xaxis, you can do this:

import matplotlib.ticker as mticker myLocator = mticker.MultipleLocator(4) ax.xaxis.set_major_locator(myLocator) 

• The issue in the OP is the dates are formatted as string type. matplotlib plots every value as a tick label with the tick location being a 0 indexed number based on the number of values.
• The resolution to this issue is to convert all values to the correct type, datetime in this case.
  • Once the axes have the correct type, there are additional matplotlib methods, which can be used to further customize the tick spacing.
• The answers to What is plotted when string data is passed to the matplotlib API? explain in more detail what happens when string values are passed to matplotlib.
• As of 2014-09-30, pandas has a read_sql function, which has a parse_dates parameter. You definitely want to use that instead.

Original Answer

Here's how you should convert your date string into real datetime objects:

import numpy as np import matplotlib.pyplot as plt import matplotlib.dates as mdates data_tuples = [ ('4084036', '1', '2006-12-22 22:46:35'), ('4084938', '1', '2006-12-23 16:19:13'), ('4084938', '2', '2006-12-23 16:20:23'), ('4084939', '1', '2006-12-23 16:29:14'), ('4084954', '1', '2006-12-23 16:28:28'), ('4250653', '1', '2007-02-12 21:58:53'), ('4250657', '1', '2007-03-12 21:58:53')] datatypes = [('col1', 'i4'), ('col2', 'i4'), ('date', 'S20')] data = np.array(data_tuples, dtype=datatypes) col1 = data['col1'] # convert the dates to a datetime type dates = mdates.num2date(mdates.datestr2num(data['date'])) fig, ax1 = plt.subplots() ax1.bar(dates, col1) fig.autofmt_xdate() 

Getting a simple list of tuples out of your database cursor should be as simple as...

data_tuples = [] for row in cursor: data_tuples.append(row) 

However, I posted a version of a function that I use to take db cursors directly to record arrays or pandas dataframes here: How to convert SQL Query result to PANDAS Data Structure?

Hopefully that helps too.

import numpy as np import pandas as pd import matplotlib.pyplot as plt # create a random dataframe with datetimeindex date_range = pd.date_range('1/1/2011', '4/10/2011', freq='D') df = pd.DataFrame(np.random.randint(0,10,size=(100, 1)), columns=['value'], index=date_range) 

Date ticklabels often overlap:

plt.plot(df.index,df['value']) plt.show() 

So it is useful to rotate them and right align them.

fig, ax = plt.subplots() ax.plot(df.index,df['value']) ax.xaxis_date() # interpret the x-axis values as dates fig.autofmt_xdate() # make space for and rotate the x-axis tick labels plt.show()