Measurement length for X and Y-axis

Question

I wonder if it's possible to change the measurement milestones for graphs created by pandas. In my code the X-axis stands for time and is measured by month, but the measurement milestones are all over the place.

In the image below, the milestones for the X-axis are 2012M01, 2012M06, 2012M11, 2013M04 and 2013M09.

Is there any way I can choose how long the distance should be between every milestone? For example, to make it so it shows every year or every half year?

This is the code I used for the function making the graph:

def graph(dataframe):
    graph = dataframe[["Profit"]].plot() 
    graph.set_title('Statistics')
    graph.set_ylabel('Thousand $')
    graph.set_xlabel('Time')
    plt.grid(True)          
    plt.show()

The actual dataframe is just an excel-file with a bunch of months and monetary values in it.

Answer 1

I think the most straight forward is to use matplotlib.dates to format the axis:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates

def graph(dataframe):
    fig, ax = plt.subplots()
    xfmt = mdates.DateFormatter('%YM%m')            #see https://strftime.org/
    major = mdates.MonthLocator([1,7])              #label only Jan and Jul

    graph = dataframe[["Profit"]].plot(ax=ax)       #link plot to the existing axes
    graph.set_title('Statistics')
    graph.set_ylabel('Thousand $')
    graph.set_xlabel('Time')
    graph.xaxis.set_major_locator(major)            #set major locator tick on x-axis
    graph.xaxis.set_major_formatter(xfmt)           #format xtick label
    plt.grid(True)          
    plt.show()

But a key point is you need to have your dates as Python's built-in datetime.date (not datetime.datetime); thanks to this answer. If your dates are str or a different type of datetime, you will need to convert, but there are many resources on SO and elsewhere for doing this like this or this:

In[0]:

dr = pd.date_range('01-01-2012', '01-01-2014', freq='1MS')
dr = [pd.to_datetime(date).date() for date in df.index]    #explicitly converting to datetime with .date()

df = pd.DataFrame(index=dr, data={'Profit':np.random.rand(25)})
type(df.index.[0])

Out[0]:
datetime.date

Calling graph(df) using the example above gets this plot:

---

Just to expand on this, here's what happens when the index is pandas.Timestamp instead of datetime.date:

In[0]:
dr = pd.date_range('01-01-2012', '01-01-2014', freq='1MS')
# dr = [pd.to_datetime(date).date() for date in df.index]       #skipping date conversion
df = pd.DataFrame(index=dr, data={'Profit':np.random.rand(25)})

graph(df)

Out[0]:

The x-axis is improperly formatted:

However, if you are willing to just create the plot directly through matplotlib, rather than pandas (pandas is using matplotlib anyway), this can handle more types of dates:

In[0]:
dr = pd.date_range('01-01-2012', '01-01-2014', freq='1MS')
# dr = [pd.to_datetime(date).date() for date in df.index]         #skipping date conversion
df = pd.DataFrame(index=dr, data={'Profit':np.random.rand(25)})

def graph_2(dataframe):
    fig, ax = plt.subplots()
    xfmt = mdates.DateFormatter('%YM%m')
    major = mdates.MonthLocator([1,7])

    ax.plot(dataframe.index,dataframe['Profit'], label='Profit')
    ax.set_title('Statistics')
    ax.set_ylabel('Thousand $')
    ax.set_xlabel('Time')
    ax.xaxis.set_major_locator(major)
    ax.xaxis.set_major_formatter(xfmt)
    ax.legend()                          #legend needs to be added
    plt.grid(True)          
    plt.show()

graph_2(df)
type(df.index[0])

Out[0]:

pandas._libs.tslibs.timestamps.Timestamp

And here is the working graph: