Python matplotlib.pyplot.pie() Examples

def analyseSex(firends):
    sexs = list(map(lambda x:x['Sex'],friends[1:]))
    counts = Counter(sexs).items()
    counts = sorted(counts, key=lambda x:x[0], reverse=False)
    counts = list(map(lambda x:x[1],counts))
    labels = ['Unknow','Male','Female']
    colors = ['red','yellowgreen','lightskyblue']
    plt.figure(figsize=(8,5), dpi=80)
    plt.axes(aspect=1) 
    plt.pie(counts, 
            labels=labels, 
            colors=colors, 
            labeldistance = 1.1, 
            autopct = '%3.1f%%',
            shadow = False, 
            startangle = 90, 
            pctdistance = 0.6 
    )
    plt.legend(loc='upper right',)
    plt.title(u'%s的微信好友性别组成' % friends[0]['NickName'])
    plt.show() 

def test_pie_frame_grid():
    # The slices will be ordered and plotted counter-clockwise.
    labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'
    sizes = [15, 30, 45, 10]
    colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral']
    # only "explode" the 2nd slice (i.e. 'Hogs')
    explode = (0, 0.1, 0, 0)

    plt.pie(sizes, explode=explode, labels=labels, colors=colors,
            autopct='%1.1f%%', shadow=True, startangle=90,
            wedgeprops={'linewidth': 0},
            frame=True, center=(2, 2))

    plt.pie(sizes[::-1], explode=explode, labels=labels, colors=colors,
            autopct='%1.1f%%', shadow=True, startangle=90,
            wedgeprops={'linewidth': 0},
            frame=True, center=(5, 2))

    plt.pie(sizes, explode=explode[::-1], labels=labels, colors=colors,
            autopct='%1.1f%%', shadow=True, startangle=90,
            wedgeprops={'linewidth': 0},
            frame=True, center=(3, 5))
    # Set aspect ratio to be equal so that pie is drawn as a circle.
    plt.axis('equal') 

def test_pie_textprops():
    data = [23, 34, 45]
    labels = ["Long name 1", "Long name 2", "Long name 3"]

    textprops = dict(horizontalalignment="center",
                     verticalalignment="top",
                     rotation=90,
                     rotation_mode="anchor",
                     size=12, color="red")

    _, texts, autopct = plt.gca().pie(data, labels=labels, autopct='%.2f',
                                      textprops=textprops)
    for labels in [texts, autopct]:
        for tx in labels:
            assert tx.get_ha() == textprops["horizontalalignment"]
            assert tx.get_va() == textprops["verticalalignment"]
            assert tx.get_rotation() == textprops["rotation"]
            assert tx.get_rotation_mode() == textprops["rotation_mode"]
            assert tx.get_size() == textprops["size"]
            assert tx.get_color() == textprops["color"] 

def label_distribution(trainFile):
    labels=[]
    with open(trainFile,'r') as f:
        raw_data=f.readlines()
        for line in raw_data:
            line=line.strip().split("+++$+++")
            label=int(line[2])
            assert label in [0,1],"Invalid label value!"
            labels.append(label)
    neg_count=labels.count(0)
    pos_count=len(labels)-neg_count
    counts=[neg_count,pos_count]
    labels=["negative","positive"]
    fig=plt.figure(figsize=(9,9))
    # 画饼图（数据，数据对应的标签，百分数保留两位小数点）
    plt.pie(counts, labels=labels, autopct='%1.2f%%')
    plt.title('Train Label Distribution', bbox={'facecolor': '0.8', 'pad': 5})
    plt.show()
    savePath=trainFile.split(".")[0]+"_ld.png"
    fig.savefig(savePath)
    plt.close() 

def graph():
    total = len(os.listdir(test_path))
    affected = pandas.read_csv('output.csv')
    affected = affected['output'].sum()

    healthy = total - affected

    piey = ['Glaucomatous', 'Healthy']
    piex = [affected, healthy]

    plt.axis('equal')
    plt.pie(piex, labels=piey, radius=1.5, autopct='%0.1f%%', explode=[0.2, 0])
    plt.show()


# Frontend GUI 

def test_pie_center_radius():
    # The slices will be ordered and plotted counter-clockwise.
    labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'
    sizes = [15, 30, 45, 10]
    colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral']
    explode = (0, 0.1, 0, 0)  # only "explode" the 2nd slice (i.e. 'Hogs')

    plt.pie(sizes, explode=explode, labels=labels, colors=colors,
            autopct='%1.1f%%', shadow=True, startangle=90,
            wedgeprops={'linewidth': 0}, center=(1, 2), radius=1.5)

    plt.annotate("Center point", xy=(1, 2), xytext=(1, 1.5),
                 arrowprops=dict(arrowstyle="->",
                                 connectionstyle="arc3"))
    # Set aspect ratio to be equal so that pie is drawn as a circle.
    plt.axis('equal') 

def pieGraphics(Labels,ValueList,graphicTitle='图例'):
    # The slices will be ordered and plotted counter-clockwise.
    #labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'
    #sizes = [15, 30, 45, 10]
    colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral', "blue","green","cyan","magenta"]
    explode = (0, 0.1, 0, 0)  # only "explode" the 2nd slice (i.e. 'Hogs')

    plt.pie(ValueList, labels=Labels, colors=colors,autopct='%1.1f%%', shadow=True, startangle=90)
    # Set aspect ratio to be equal so that pie is drawn as a circle.
    plt.axis('equal')
    plt.show()


#barGraphics('等级','数量',['A','B','C','D','E','F'],[29,30,40,47,38,23],'测试图例')
#linePlotGraphics("xLabel","yLabel",[1,2,3,4,5,6,7,8,9,10],[1.1,1.9,2.6,3.6,9.8,14,24,40,80,150],graphicTitle='图例')
#scatterPlotsGraphics("xLabel","yLabel",[1,2,3,4,5,6,7,8,9,10],[1,11.9,2,6.3,6,9.8,14,4,8,5],graphicTitle='图例') 

def pieGraphics(Labels,ValueList,graphicTitle='图例'):
    colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral', "blue","green","cyan","magenta"]
    maxdata = max(ValueList)
    explode = []
    for v in ValueList:
        if v == maxdata:
            explode.append(0.1)
        else:
            explode.append(0)
    print(explode)
    patches,l_text,p_text = plt.pie(ValueList, labels=Labels, colors=colors,autopct='%1.1f%%',explode=explode ,shadow=True, startangle=90)
    for font in l_text:
        font.set_fontproperties(FontProperties(fname=PATH_SUFFIX+'SIMLI.TTF'))
    plt.title(graphicTitle,fontproperties=font_set,y=1.05)
    # Set aspect ratio to be equal so that pie is drawn as a circle.
    plt.axis('equal')
    plt.show() 

def pieGraphics(Labels,ValueList,graphicTitle='图例'):
    colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral', "blue","green","cyan","magenta"]
    maxdata = max(ValueList)
    explode = []
    for v in ValueList:
        if v == maxdata:
            explode.append(0.1)
        else:
            explode.append(0)
    print(explode)
    patches,l_text,p_text = plt.pie(ValueList, labels=Labels, colors=colors,autopct='%1.1f%%',explode=explode ,shadow=True, startangle=90)
    for font in l_text:
        font.set_fontproperties(FontProperties(fname=PATH_SUFFIX+'SIMLI.TTF'))
    plt.title(graphicTitle,fontproperties=font_set,y=1.05)
    # Set aspect ratio to be equal so that pie is drawn as a circle.
    plt.axis('equal')
    plt.show() 

def test_pie_textprops():
    data = [23, 34, 45]
    labels = ["Long name 1", "Long name 2", "Long name 3"]

    textprops = dict(horizontalalignment="center",
                     verticalalignment="top",
                     rotation=90,
                     rotation_mode="anchor",
                     size=12, color="red")

    _, texts, autopct = plt.gca().pie(data, labels=labels, autopct='%.2f',
                                      textprops=textprops)
    for labels in [texts, autopct]:
        for tx in labels:
            assert tx.get_ha() == textprops["horizontalalignment"]
            assert tx.get_va() == textprops["verticalalignment"]
            assert tx.get_rotation() == textprops["rotation"]
            assert tx.get_rotation_mode() == textprops["rotation_mode"]
            assert tx.get_size() == textprops["size"]
            assert tx.get_color() == textprops["color"] 

def pieGraphics(Labels,ValueList,graphicTitle='图例'):
    colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral', "blue","green","cyan","magenta"]
    maxdata = max(ValueList)
    explode = []
    for v in ValueList:
        if v == maxdata:
            explode.append(0.1)
        else:
            explode.append(0)
    print(explode)
    patches,l_text,p_text = plt.pie(ValueList, labels=Labels, colors=colors,autopct='%1.1f%%',explode=explode ,shadow=True, startangle=90)
    for font in l_text:
        font.set_fontproperties(FontProperties(fname=PATH_SUFFIX+'SIMLI.TTF'))
    plt.title(graphicTitle,fontproperties=font_set,y=1.05)
    # Set aspect ratio to be equal so that pie is drawn as a circle.
    plt.axis('equal')
    plt.show() 

def visualize_distributions(distro_dict, threshold=10):
    import matplotlib.pyplot as plt
    key_list = sorted(distro_dict.keys())
    threshold_str = '%d+' % (threshold, )
    label_list = [
        threshold_str if key == threshold else str(key)
        for key in key_list
    ]
    size_list = [ distro_dict[key] for key in key_list ]
    color_list = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral']
    explode = [0.0] + [0.0] * (len(size_list) - 1)

    plt.pie(size_list, explode=explode, labels=label_list, colors=color_list,
            autopct='%1.1f%%', shadow=True, startangle=90)
    plt.axis('equal')
    plt.show() 

def analysis_mobile(self):
        # self.record_result('<strong style="color: black; font-size: 24px;">正在分析该商品不同省份的购买量...</strong>')

        fig_size = plt.rcParams["figure.figsize"]
        plt.figure(figsize = (2.4, 2.4))

        obj = self.data_frame['is_mobile']
        obj = obj.value_counts()

        obj = obj.rename({1: '移动端', 0: 'PC'})
        plt.pie(x = obj.values, autopct = '%.0f%%', radius = 0.7, labels = obj.index, startangle = 180)

        plt.title('该商品移动/ PC 购买比例')

        plt.tight_layout()
        filename = '%s_mobile.png' % self.product_id
        plt.savefig('%s/%s' % (utils.get_save_image_path(), filename))
        plt.figure(figsize = fig_size)
        plt.clf()
        result = utils.get_image_src(filename = filename)
        self.record_result(result, type = 'image')

    # 分析购买后评论的时间分布 

def plot(sizes,plotname):
    fig = plt.figure(figsize=(2.0,2.0),facecolor='white')
    ax = plt.subplot(111)
    psizes = ['%1.1f%%' % (x/sum(sizes)*100) for x in sizes]
    labels = [x+'\n'+y for x,y in zip(LABELS,psizes)]
    patches = plt.pie(sizes, colors=COLORS, labels=labels, 
                      shadow=False, startangle=90, labeldistance=0.7, 
                      wedgeprops={'linewidth': 4})
    for pie_wedge in patches[0]:
        pie_wedge.set_edgecolor('white')
    for t in patches[1]:
        t.set_horizontalalignment('center')
    plt.axis('equal')
    plt.tight_layout()
    plt.savefig(plotname)
    print('saved plot to {}'.format(plotname))
    plt.show()


######################################################
# Data processing 

def test_pie_center_radius():
    # The slices will be ordered and plotted counter-clockwise.
    labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'
    sizes = [15, 30, 45, 10]
    colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral']
    explode = (0, 0.1, 0, 0)  # only "explode" the 2nd slice (i.e. 'Hogs')

    plt.pie(sizes, explode=explode, labels=labels, colors=colors,
            autopct='%1.1f%%', shadow=True, startangle=90,
            wedgeprops={'linewidth': 0}, center=(1, 2), radius=1.5)

    plt.annotate("Center point", xy=(1, 2), xytext=(1, 1.5),
                 arrowprops=dict(arrowstyle="->",
                                 connectionstyle="arc3"))
    # Set aspect ratio to be equal so that pie is drawn as a circle.
    plt.axis('equal') 

def pieGraphics(Labels,ValueList,graphicTitle='图例'):
    # The slices will be ordered and plotted counter-clockwise.
    #labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'
    #sizes = [15, 30, 45, 10]
    colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral', "blue","green","cyan","magenta"]
    explode = (0, 0.1, 0, 0)  # only "explode" the 2nd slice (i.e. 'Hogs')

    plt.pie(ValueList, labels=Labels, colors=colors,autopct='%1.1f%%', shadow=True, startangle=90)
    # Set aspect ratio to be equal so that pie is drawn as a circle.
    plt.axis('equal')
    plt.show()


#barGraphics('等级','数量',['A','B','C','D','E','F'],[29,30,40,47,38,23],'测试图例')
#linePlotGraphics("xLabel","yLabel",[1,2,3,4,5,6,7,8,9,10],[1.1,1.9,2.6,3.6,9.8,14,24,40,80,150],graphicTitle='图例')
#scatterPlotsGraphics("xLabel","yLabel",[1,2,3,4,5,6,7,8,9,10],[1,11.9,2,6.3,6,9.8,14,4,8,5],graphicTitle='图例') 

def show_pie_image():
    plt.figure(figsize=(10, 16))
    size = [10, 25, 20, 45]
    colors = ["red", "green", "blue", "yellow"]
    explode = [0, 0.05, 0, 0.1]
    labels = ["apple", "pears", "grape", "bananas"]
    plt.pie(
        x=size,
        labels=labels,
        colors=colors,
        explode=explode
    )
    plt.axis("equal")
    plt.show() 

def test_pie_linewidth_0():
    # The slices will be ordered and plotted counter-clockwise.
    labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'
    sizes = [15, 30, 45, 10]
    colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral']
    explode = (0, 0.1, 0, 0)  # only "explode" the 2nd slice (i.e. 'Hogs')

    plt.pie(sizes, explode=explode, labels=labels, colors=colors,
            autopct='%1.1f%%', shadow=True, startangle=90,
            wedgeprops={'linewidth': 0})
    # Set aspect ratio to be equal so that pie is drawn as a circle.
    plt.axis('equal')

    # Reuse testcase from above for a labeled data test
    data = {"l": labels, "s": sizes, "c": colors, "ex": explode}
    fig = plt.figure()
    ax = fig.gca()
    ax.pie("s", explode="ex", labels="l", colors="c",
           autopct='%1.1f%%', shadow=True, startangle=90,
           wedgeprops={'linewidth': 0}, data=data)
    ax.axis('equal')

    # And again to test the pyplot functions which should also be able to be
    # called with a data kwarg
    plt.figure()
    plt.pie("s", explode="ex", labels="l", colors="c",
            autopct='%1.1f%%', shadow=True, startangle=90,
            wedgeprops={'linewidth': 0}, data=data)
    plt.axis('equal') 

def describe_recursion(frame):
    typ_cnt = [0, 0, 0, 0]
    for P_ in frame:
        for P in P_:
            if len(P[-2]) == 0 and len(P[-3]) == 0:
                continue
            typ = 2 * (~P[0]) + (len(P[-2]) > 0)
            typ_cnt[typ] += 1
    labels = ['rng', 'rng_seg', 'der', 'der_seg']
    plt.pie(typ_cnt, labels=labels) 

def test_pie_ccw_true():
    # The slices will be ordered and plotted counter-clockwise.
    labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'
    sizes = [15, 30, 45, 10]
    colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral']
    explode = (0, 0.1, 0, 0)  # only "explode" the 2nd slice (i.e. 'Hogs')

    plt.pie(sizes, explode=explode, labels=labels, colors=colors,
            autopct='%1.1f%%', shadow=True, startangle=90,
            counterclock=True)
    # Set aspect ratio to be equal so that pie is drawn as a circle.
    plt.axis('equal') 

def test_pie_linewidth_2():
    # The slices will be ordered and plotted counter-clockwise.
    labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'
    sizes = [15, 30, 45, 10]
    colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral']
    explode = (0, 0.1, 0, 0)  # only "explode" the 2nd slice (i.e. 'Hogs')

    plt.pie(sizes, explode=explode, labels=labels, colors=colors,
            autopct='%1.1f%%', shadow=True, startangle=90,
            wedgeprops={'linewidth': 2})
    # Set aspect ratio to be equal so that pie is drawn as a circle.
    plt.axis('equal') 

def make_chart_pie_chart(plt):

    plt.pie([0.95, 0.05], labels=["Uses pie charts", "Knows better"])

    # make sure pie is a circle and not an oval
    plt.axis("equal")
    plt.show() 

def score(self):
        print("Calculating Your Activity Score.....")
        colors = ["#ff3300", "#33cc33"]
        score_value = round(
            math.log(
                (
                    len(urls)
                    + len(search_clean * 2)
                    + len(all_likes * 3)
                    + len(all_links * 4)
                )
                / 9,
                1.12,
            ),
            1,
        )
        x_0 = [1, 0, 0, 0]
        pl.pie([100 - score_value, score_value], autopct="%1.1f%%", startangle=90, colors=colors, pctdistance=10)
        plt.pie(x_0, radius=0.7, colors="w")
        plt.axis("equal")

        plt.title("Your YouTube Activity Score",
                  fontsize=21,
                  color="steelblue",
                  fontweight="bold",
                  fontname="Arial")

        plt.annotate(score_value,
                     (0, 0), (123, 154),
                     fontsize=54,
                     color="teal",
                     fontweight="bold",
                     fontname="Arial",
                     xycoords="axes fraction",
                     textcoords="offset points",
                     va="top")
        plt.savefig(os.path.join(image_dir,"score.png"), dpi=400)
        plt.clf() 

def plot_pie(data, pathOut, title=""):

    labels = list(data.keys())
    sizes = [data[x] for x in labels]

    plt.clf()
    plt.style.use('classic')
    patches, texts, _ = plt.pie(sizes, autopct=lambda p: '{:.0f}'.format(p * sum(sizes) / 100),
                                shadow=False, startangle=90, pctdistance=1.1)
    #plt.axes([0.3, 0.3, .5, .5])
    plt.legend(patches, labels, loc='lower right',
               fontsize=8)
    plt.tight_layout()
    plt.title(title)
    plt.savefig(pathOut, bbox_inches='tight') 

def make_chart_pie_chart():

    plt.pie([0.95, 0.05], labels=["Uses pie charts", "Knows better"])

    # make sure pie is a circle and not an oval
    plt.axis("equal")
    plt.show() 

def plot_donut(df):
    """Generates a donut plot with the counts of 3 categories.

    Parameters
    ----------
    df : pandas.DataFrame
        The DataFrame to be plotted.

    """

    # We will only need 3 categories and 3 values.
    labels = ["Positivo", "Negativo", "Neutro"]

    positive = len(df[df["score"] > 0])
    negative = len(df[df["score"] < 0])
    neutral = len(df[df["score"] == 0])

    values = [positive, negative, neutral]
    colors = ["green", "orange", "yellow"]
    explode = (0, 0, 0)  # Explode a slice if required

    plt.rcParams["font.size"] = 18
    plt.rcParams["legend.fontsize"] = 20

    plt.pie(values, explode=explode, labels=None,
            colors=colors, autopct='%1.1f%%', shadow=False)

    # We draw a circle in the Pie chart to make it a donut chart.
    centre_circle = plt.Circle(
        (0, 0), 0.75, color="#5C0E10", fc="#5C0E10", linewidth=0)

    fig = plt.gcf()
    fig.gca().add_artist(centre_circle)

    plt.axis("equal")
    plt.legend(labels)
    plt.savefig("donut.png",  facecolor="#5C0E10") 

def test_pie_linewidth_0():
    # The slices will be ordered and plotted counter-clockwise.
    labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'
    sizes = [15, 30, 45, 10]
    colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral']
    explode = (0, 0.1, 0, 0) # only "explode" the 2nd slice (i.e. 'Hogs')

    plt.pie(sizes, explode=explode, labels=labels, colors=colors,
            autopct='%1.1f%%', shadow=True, startangle=90,
            wedgeprops={'linewidth': 0})
    # Set aspect ratio to be equal so that pie is drawn as a circle.
    plt.axis('equal') 

def get_pie_data(self, data):
        ''' Return the labels and sizes needed for the pie chart '''
        labels = [v[0] for v in data]
        sizes = [v[1] for v in data]
        return labels, sizes 

def plot_pie(self, key, data=None):
        ''' Makes a matplotlib pie chart for a given tag key '''
        if self.tagvals and key in self.tagvals:
            vals = self.tagvals[key]
            labels = [v['name'] for v in vals]
            sizes = [v['count'] for v in vals]
        elif key == 'place':
            assert data is not None, 'Must provide some data to extract'
            vals = self.count_data(data)
            labels, sizes = self.get_pie_data(vals)
        elif key == 'baseurl':
            vals = self.tagvals['url']
            names = [v['name'] for v in vals]
            labels = set(['sas' if 'sas.sdss.org' in n else 'api' for n in names])
            stats = [{'type': 'web' if l == 'sas' else l, 'count': sum(d['count'] for d in vals if l in d['name'])} for l in set(labels)]
            labels = [s['type'] for s in stats]
            sizes = [s['count'] for s in stats]
        else:
            sizes = None
            raise NameError('No sizes or labels found.  Make sure the data is somewhere')

        plt.clf()
        plt.pie(sizes, labels=labels, shadow=True, autopct='%1.1f%%', startangle=90)
        plt.axis('equal')
        plt.tight_layout()
        plt.show() 

def main(subfile,plotname):
    # read in ground truth, and plot the distribution 
    # of the top scores for each class
    sub = loadfile(subfile)
    subdict = dict(sub)
    with open(GTFILE) as f:
        reader = csv.DictReader(f)
        for row in reader:
            aid = row['id']
            length = float(row['length'])
            Nframes = FPS*int(length)
            actions = parse_actions(row['actions'])
            frames = np.linspace(0,length,25)
            framenumber = np.linspace(0,Nframes,25)
            for f,sec,fnum in zip(range(1,26),frames,framenumber):
                analyze(subdict,actions,aid,f,sec,fnum)
    pies = []
    for c in range(157):
        scores = det[c]
        scores.sort(key=lambda x: -x[2])
        scores = scores[:100] # pick the top 100 scores for this class
        tp = sum(1 for x in scores if x[4]=='tp')
        bnd = sum(1 for x in scores if x[4]=='bnd')
        fb = sum(1 for x in scores if x[4]=='fb')
        obj = fb/2.+sum(1 for x in scores if x[4]=='obj')
        vrb = fb/2.+sum(1 for x in scores if x[4]=='vrb')
        fo = sum(1 for x in scores if x[4]=='fo')
        fp = sum(1 for x in scores if x[4]=='fp')
        pie = [tp, bnd, obj, vrb, fo, fp]
        pies.append(pie)
    sizes = [sum(x) for x in zip(*pies)] #combine results for all classes
    plot(sizes,plotname)