Matplotlib Save Figure
After creating a plot or chart using the python matplotlib library and need to save and use it further. Then the matplotlib savefig function will help you. In this blog, we are explaining, how to save a figure using matplotlib?
Import Library
| 1 | import matplotlib.pyplot as plt # for data visualization |
Matplotlib SaveFig (save figure) Different ways
Syntax: plt.savefig(
“File path with name or name”,
dpi=None,
quality = 99,
facecolor=’w’,
edgecolor=’w’,
orientation=’portrait’,
papertype=None,
format=None,
transparent=False,
bbox_inches=None,
pad_inches=0.1,
frameon=None,
metadata=None,
)
Recommended Value Type for Parameters
fname : str or file-like object
dpi : [ *None* | scalar > 0 | ‘figure’ ]quality : [ *None* | 1 <= scalar <= 100 ]
facecolor : color spec or None, optional
edgecolor : color spec or None, optional
orientation : {‘landscape’, ‘portrait’}
papertype : str
— ‘letter’, ‘legal’, ‘executive’, ‘ledger’, ‘a0’ through
‘a10’, ‘b0’ through ‘b10’
format : str —png, pdf, ps, eps and svg
transparent : bool
frameon : bool
bbox_inches : str or `~matplotlib.transforms.Bbox`, optional
pad_inches : scalar, optional
bbox_extra_artists : list of `~matplotlib.artist.Artist`, optional
metadata : dict, optional
Here, we are creating a simple pie chart and save it using plt.savefig() function. The file saves at program file location by default with “.png” format. You can change the file path.
| 123 | plt.pie([40,30,20]) # plot pie chartplt.savefig("pie_char") # save above pie chart with name pie_chartplt.show() |
Output >>>
Saved Image >>>
Save Matplolib Figure using some parameters
| 1234567 | plt.pie([40,30,20])plt.savefig("pie_char2", # file namedpi = 100, # dot per inch for resolution increase value for more resolutionquality = 99, # "1 <= value <= 100" 100 for best qulityfacecolor = "g" # image background color)plt.show() |
Output >>>
Saved Image >>>
Example:
In bellow example, create plots and charts and show using subplot function. Then line no. “105” (plt.savefig(“D:\\subplot_figure.png”)) save this subplot at user define location.
Output >>>
plt.figure(figsize=(23,27))
##----------------------------------------start
#plt.subplot(3,2,1)
plt.subplot(321)
#********************************************Line Plot
days = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]
delhi_tem = [36.6, 37, 37.7,39,40.1,43,43.4,45,45.6,40.1,44,45,46.8,47,47.8]
mumbai_tem = [39,39.4,40,40.7,41,42.5,43.5,44,44.9,44,45,45.1,46,47,46]
plt.plot(days, delhi_tem, "mo--", linewidth = 3,
markersize = 10, label = "Delhi tem")
plt.plot(days, mumbai_tem, "yo:", linewidth = 3,
markersize = 10, label = "Mumbai tem}")
plt.title("Delhi & Mumbai Temperature Line Plot", fontsize=15)
plt.xlabel("days",fontsize=13)
plt.ylabel("temperature",fontsize=13)
plt.legend(loc = 4)
plt.grid(color='w', linestyle='-', linewidth=2)
#---------------------------------------------------------------end
plt.subplot(3,2,2) ##-------------------------------------------------start
#****************************************************************histograms
ml_students_age = np.random.randint(18,45, (100))
py_students_age = np.random.randint(15,40, (100))
bins = [15,20,25,30,35,40,45]
plt.hist([ml_students_age, py_students_age], bins, rwidth=0.8, histtype = "bar",
orientation='vertical', color = ["m", "y"], label = ["ML Student", "Py Student"])
plt.title("ML & Py Students age histograms")
plt.xlabel("Students age cotegory")
plt.ylabel("No. Students age")
plt.legend()
#----------------------------------------------------------------------end
plt.subplot(3,2,3) ##--------------------------------------------start
#************************************************************Bar Chart
classes = ["Python", "R", "AI", "ML", "DS"]
class1_students = [30, 10, 20, 25, 10] # out of 100 student in each class
class2_students = [40, 5, 20, 20, 10]
class3_students = [35, 5, 30, 15, 15]
classes_index = np.arange(len(classes))
width = 0.2
plt.barh(classes_index, class1_students, width , color = "b",
label =" Class 1 Students") #visible=False
plt.barh(classes_index + width, class2_students, width , color = "g",
label =" Class 2 Students")
plt.barh(classes_index + width + width, class3_students, width , color = "y",
label =" Class 3 Students")
plt.yticks(classes_index + width, classes, rotation = 20)
plt.title("Bar Chart of IAIP Class Bar Chart", fontsize = 18)
plt.ylabel("Classes",fontsize = 15)
plt.xlabel("No. of Students", fontsize = 15)
plt.legend()
#--------------------------------------------------------------------end
plt.subplot(3,2,4) ##------------------------------------------------start
#**************************************************************Scatter Plot
df_google_play_store_apps = pd.read_csv("D:\\Private\Indina AI Production\Kaggel Dataset\google-play-store-apps\googleplaystore.csv", nrows = 1000)
x = df_google_play_store_apps["Rating"]
y = df_google_play_store_apps["Reviews"]
plt.scatter(x,y, c = "r", marker = "*", s = 100, alpha=0.5, linewidths=10,
edgecolors="g" )#verts="<"
plt.scatter(x,df_google_play_store_apps["Installs"], c = "y", marker = "o", s = 100, alpha=0.5, linewidths=10,
edgecolors="c" )
plt.title("Google Play Store Apps Scatter plot")
plt.xlabel("Rating")
plt.ylabel("Reviews & Installs")
#----------------------------------------------------------------------end
plt.subplot(3,2,5) ##-----------------------------------------start
#*************************************************************Pie plot
classes = ["Python", 'R', 'Machine Learning', 'Artificial Intelligence',
'Data Sciece']
class1_students = [45, 15, 35, 25, 30]
explode = [0.03,0,0.1,0,0]
colors = ["c", 'b','r','y','g']
textprops = {"fontsize":15}
plt.pie(class1_students,
labels = classes,
explode = explode,
colors =colors,
autopct = "%0.2f%%",
shadow = True,
radius = 1.4,
startangle = 270,
textprops =textprops)
#------------------------------------------------------end
plt.subplot(3,2,6, projection='polar', facecolor='k' ,frameon=True)
plt.savefig("D:\\subplot_figure.png") # save subplots at drive "D" of name subplot_figure
plt.show()
Output >>>
Saved Image >>>
CONCLUSION
In the matplotlib save figure blog, we learn how to save figure with a real-time example using the plt.savefig() function. Along with that used different method and different parameter. We suggest you make your hand dirty with each and every parameter of the above methods. This is the best coding practice. After completion of the matplotlib tutorial jump on Seaborn.
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