CODEWITHSUNDEEP
pythonpandasmatplotlibbase64
lccrva
lccrva

Reputation: 3

Adjusting size of matplot lib figure (including title and tick labels)- hidden when sizing down figure

I'm creating an html page with a dropdown menu. When the user hits the "submit" button after making their selection from the dropdown menu, the cgi script runs and pulls data from a csv file, plots it using matplotlib, and then displays the plot using base64. The plot has dates along the x-axis and percentage on the y-axis.

I've got it all working in python 3.8 using spyder, but when I load it to my server (which uses python 3.4) it creates a huge plot that I have to scroll on the browser. When I change the figsize to height less than 10, it cuts off the x-axis label and tick labels. I've rotated the xticks 30* to make them readable. How do I essentially "zoom out" on the entire figure including tick & axis labels?

Here's the portion of my code that creates the plot:

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
import base64

    fig, ax = plt.subplots(figsize=(15, 10))  
    df = pd.read_csv(filepath, header=1, parse_dates=['Report_Date'], index_col=['Report_Date'])  
    ax.plot(df.index.values, df['colname'], color='teal')  
    ax.yaxis.set_major_formatter(mtick.PercentFormatter(xmax=1, decimals=None, symbol='%', is_latex=False))   
    plt.xlabel('Report Date')
    plt.ylabel('ylabel')
    plt.title('title') 
    plt.xticks(rotation=30, ha='right')   
    plt.savefig('picture.png', dpi=200)  
    data_uri = base64.b64encode(open('picture.png','rb').read()).decode('utf-8')  
    img_tag = '<img src='data:image/png;base64,{0}>'.format(data_uri)  
    print(img_tag)

Upvotes: 0

Views: 461

Answers (1)

Panda50
Panda50

Reputation: 939

I think that the simplest way for you is to add plt.tight_layout before plt.savefig

like this:

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
import base64

fig, ax = plt.subplots(figsize=(15, 10))  
df = pd.read_csv(filepath, header=1, parse_dates=['Report_Date'], index_col=['Report_Date'])  
ax.plot(df.index.values, df['colname'], color='teal')  
ax.yaxis.set_major_formatter(mtick.PercentFormatter(xmax=1, decimals=None, symbol='%', is_latex=False))   
plt.xlabel('Report Date')
plt.ylabel('ylabel')
plt.title('title') 
plt.xticks(rotation=30, ha='right')
plt.tight_layout()
plt.savefig('picture.png', dpi=200)  
data_uri = base64.b64encode(open('picture.png','rb').read()).decode('utf-8')  
img_tag = '<img src='data:image/png;base64,{0}>'.format(data_uri)  
print(img_tag)

more info about it : https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.tight_layout.html

Upvotes: 1

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