def plot_pie(self, nx_dataframe, topic, se_title, kurs):
gender = self.sort_column(nx_dataframe[topic])
labels = [gender[0][i] for i,elem in enumerate(gender[0])]
fracs = [gender[1][i] for i,elem in enumerate(gender[1])]
explode = [0.05 for i,elem in enumerate(gender[1])]
plt.pie(fracs, explode=explode, labels=labels, autopct='%.0f%%', shadow=True)
plt.savefig('./PDFcreater/Plots/{}/1{}.png'.format(kurs,se_title))
plt.clf()
plt.cla()
plt.close()
def comparision_piechart(username):##function declaration to show number of positive and negative comments and plot a pie-chart
media_id = fetch_post_id(username)
request_url = (BASE_URL + 'media/%s/comments/?access_token=%s') % (media_id, app_access_token)
print 'GET request url : %s' % (request_url)
comment_info = requests.get(request_url).json()
if comment_info['meta']['code'] == 200:
if len(comment_info['data']):
for x in range(0, len(comment_info['data'])):
comment_id = comment_info['data'][x]['id']
comment_text = comment_info['data'][x]['text']
blob = TextBlob(comment_text, analyzer=NaiveBayesAnalyzer())
if (blob.sentiment.p_neg > blob.sentiment.p_pos):
print 'Negative comment : %s' % (comment_text)
for y in range(0, len(comment_info['data'])):
comment_id = comment_info['data'][y]['id']
comment_text = comment_info['data'][y]['text']
blob = TextBlob(comment_text, analyzer=NaiveBayesAnalyzer())
if (blob.sentiment.p_neg < blob.sentiment.p_pos):
print 'positive comment : %s' % (comment_text)
a = y + 1 # positive comments
b = x - y # negative comments
print "No. of Positive comments: %s" % (a)
print "No. of negative comments: %s" % (b)
c = a + b
print "Total no. of comments: %s" %(c)
#commands to plot pychart
labels = 'Positive ', 'Negative'
sizes = [a, b]
colors = ['blanchedalmond', 'aliceblue']
explode = (0.1, 0) # explode 1st slice
plt.pie(sizes, explode=explode, labels=labels, colors=colors,
autopct='%1.1f%%', shadow=True, startangle=140)
plt.axis('equal')
plt.show()
else:
print 'Comments not found on the post!'
else:
print 'Status code other than 200 received!'
def fetch(Handle):
root.destroy()
summary = {
"Compilation": 0,
"Runtime": 0,
"Wrong": 0,
"Accepted": 0,
"Time": 0
}
url = "https://codeforces.com/submissions/" + Handle + "/page/"
source = req.get(url + "1")
soup = bs(source.text, "lxml")
no = len(soup.find_all("span", class_="page-index"))
page_no = i = 1
while page_no <= no:
source = req.get(url + str(page_no))
soup = bs(source.text, "lxml")
table = soup.find_all("tr")
page_no = page_no + 1
for tag in range(26, len(table) - 1):
required = table[tag].find_all("td", class_="status-small")
contest_name = required[1].a.text.strip()
try:
result = required[2].span.span.text
except:
result = required[2].span.text
result = result.split(" ")
length = len(result)
if length == 1:
print(Fore.GREEN + str(i) + " : " + contest_name)
if length == 2:
print(Fore.CYAN + str(i) + " : " + contest_name)
if length == 6:
print(Fore.BLUE + str(i) + " : " + contest_name)
if length == 5:
if result[0] == "Wrong":
print(Fore.RED + str(i) + " : " + contest_name)
else:
print(Fore.WHITE + str(i) + " : " + contest_name)
summary[result[0]] += 1
print(Style.RESET_ALL, end="")
i = i + 1
plt.axis("equal")
plt.pie(summary.values(), labels=summary.keys(), autopct=None)
plt.show()
def getHistogram2PGN(df):
y = df.iloc[:, -1]
c = Counter(y)
numDiffClasses = len(y.unique())
target_names = y.unique()
colors = colors = ['lightcoral', 'gold', 'yellowgreen',
'cyan'][:numDiffClasses]
plt.rcParams["figure.figsize"] = [10, 5]
plt.pie([c[i] / len(y) * 100.0 for i in c],
labels=target_names,
colors=colors,
autopct='%1.1f%%',
shadow=True,
startangle=90)
plt.axis('equal')
plt.title(df.columns.values[-1])
path = WORKING_PATH + "/___circle_labels.png"
plt.savefig(path)
plt.clf()
return path
def pieChart(df):
stress_groups = df.groupby("Stress")
one_ser = stress_groups.get_group(1)
two_ser = stress_groups.get_group(2)
three_ser = stress_groups.get_group(3)
four_ser = stress_groups.get_group(4)
five_ser = stress_groups.get_group(5)
six_ser = stress_groups.get_group(6)
seven_ser = stress_groups.get_group(7)
eight_ser = stress_groups.get_group(8)
nine_ser = stress_groups.get_group(9)
plt.figure(figsize=(8,8))
# define x and y values
xs = [1, 2, 3, 4,5, 6, 7, 8, 9]
ys = [len(one_ser),len(two_ser),len(three_ser),len(four_ser),len(five_ser),len(six_ser),len(seven_ser),len(eight_ser),len(nine_ser),]
#giving plot a label for clarity
plt.title('Percent of days at stress levels (1-9)')
#creating pie chart
plt.pie(ys, labels=xs, autopct="%1.1f%%")
plt.show()
#plotting the pollutant data with the help of bar chart
pollutants = [i for i in iaqi]
values = [i['v'] for i in iaqi.values()]
# Exploding the first slice
explode = [0 for i in pollutants]
mx = values.index(max(values)) # explode 1st slice
explode[mx] = 0.1
# Plot a pie chart
plt.figure(figsize=(8, 6))
plt.pie(values,
labels=pollutants,
explode=explode,
autopct='%1.1f%%',
shadow=True)
plt.title('Air pollutants and their probable amount in atmosphere [India]')
plt.axis('equal')
plt.show()
# # showing INDIA AQI on world map using cartopy
# In[82]:
import cartopy.crs as ccrs
# In[83]:
def main(argv):
print("hello")
# 读取表格
data = xlrd.open_workbook("developers.xlsx")
# 获取表格的sheets
table = data.sheets()[0]
# 输出行数量
print(table.nrows) # 8
# 输出列数量
print(table.ncols) # 4
# 获取第一行数据
row1data = table.row_values(0)
print(row1data) # ['列1', '列2', '列3', '列4']
print(row1data[0]) # 列1
from pyecharts.charts import Bar
# 读取表格
# data = xlrd.open_workbook("developers.xlsx")
# 获取表格的sheets
table = data.sheets()[0]
# 输出行数量
print(table.nrows)
# 输出列数量
print(table.ncols)
# 获取第一行数据
row1data = table.row_values(0)
print(row1data) # ['列1', '列2', '列3', '列4']
print(row1data[0]) # 列1
xdata = []
ydata = []
for i in range(1, table.nrows):
print(table.row_values(i))
xdata.append(table.row_values(i)[0])
ydata.append(table.row_values(i)[1])
print(xdata)
print(ydata)
# 数据可视化，柱状图
bar = Bar()
bar.add_xaxis(xdata)
bar.add_yaxis("名称1", ydata)
bar.render("show.html")
plt.bar(xdata, ydata)
x = [2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019]
y = [5, 3, 6, 20, 17, 16, 19, 30, 32, 35]
plt.plot(x, y)
plt.show()
a = np.random.randn(100)
s = pd.Series(a)
plt.hist(s)
plt.show()
x = ['Cat1', 'Cat2', 'Cat3', 'Cat4', 'Cat5']
y = [5, 4, 8, 12, 7]
plt.bar(x, y)
plt.show()
x = ['Cat1', 'Cat2', 'Cat3', 'Cat4', 'Cat5']
y = [5, 4, 8, 12, 7]
plt.barh(x, y)
plt.show()
nums = [25, 37, 33, 37, 6]
labels = ['High-school', 'Bachelor', 'Master', 'Ph.d', 'Others']
plt.pie(x=nums, labels=labels)
plt.show()
# 生成0-1之间的10*4维度数据
data = np.random.normal(size=(10, 4))
lables = ['A', 'B', 'C', 'D']
# 用Matplotlib画箱线图
plt.boxplot(data, labels=lables)
plt.show()
# flights = sns.load_dataset("flights")
# data = flights.pivot('year', 'month', 'passengers')
# sns.heatmap(data)
# plt.show()
N = 1000
x = np.random.randn(N)
y = np.random.randn(N)
plt.scatter(x, y, marker='x')
plt.show()
N = 10000
x = np.random.randn(N)
y = np.random.randn(N)
plt.scatter(x, y, marker='x')
plt.show()
labels = np.array([u"推进", "KDA", u"生存", u"团战", u"发育", u"输出"])
stats = [83, 61, 95, 67, 76, 88]
# 画图数据准备，角度、状态值
angles = np.linspace(0, 2 * np.pi, len(labels), endpoint=False)
stats = np.concatenate((stats, [stats[0]]))
angles = np.concatenate((angles, [angles[0]]))
# 用Matplotlib画蜘蛛图
fig = plt.figure()
ax = fig.add_subplot(111, polar=True)
ax.plot(angles, stats, 'o-', linewidth=2)
ax.fill(angles, stats, alpha=0.25)
# 设置中文字体
# font = FontProperties(fname=r"/System/Library/Fonts/PingFang.ttc", size=14)
# ax.set_thetagrids(angles * 180/np.pi, labels, FontProperties=font)
plt.show()
# tips = sns.load_dataset("tips")
# tips.head(10)
# # 散点图
# sns.jointplot(x="total_bill", y="tip", data=tips, kind='scatter')
#
# # Hexbin图
# sns.jointplot(x="total_bill", y="tip", data=tips, kind='hex')
# plt.show()
df = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd'])
# 堆面积图
df.plot.area()
# 面积图
df.plot.area(stacked=False)
df = pd.DataFrame(np.random.randn(1000, 2), columns=['a', 'b'])
df['b'] = df['b'] + np.arange(1000)
# 关键字参数gridsize；它控制x方向上的六边形数量，默认为100，较大的gridsize意味着更多，更小的bin
df.plot.hexbin(x='a', y='b', gridsize=25)
print("end")
typ)
new1['sent_scores'] = sentscore(new1)
############################ Pie charts for top features ################################
import matplotlib.pyplot as plt
title = locfeatures[6][0]
imps = [
locfeatures[6][1][0][1], locfeatures[6][1][1][1], locfeatures[6][1][2][1]
]
labels = [
locfeatures[6][1][0][0], locfeatures[6][1][1][0], locfeatures[6][1][2][0]
]
sizes = [i / sum(imps) for i in imps]
a = plt.pie(sizes, shadow=True, startangle=90)
l = [
str(a) + ', ' + str(round(b * 100, 2)) + '%'
for a, b in list(zip(labels, sizes))
]
plt.legend(a[0], l, bbox_to_anchor=(1.35, 0.025), loc="lower right")
plt.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
plt.title(title)
plt.show()
title = locfeatures[19][0]
imps = [
locfeatures[19][1][0][1], locfeatures[19][1][1][1],
locfeatures[19][1][2][1]
import matplotlib as plt import numpy as np y = np.array([35, 25, 25, 15]) mylabels = ['Apples', 'Bananas', 'Charries', 'Dates'] myexplode = [0.2, 0, 0, 0] plt.pie(y, labels=mylebels, explode=myexplode, shadow=True)
# These codes are adapted from the lecture PowerPoint, thanks for providing these useful examples.
# And I also look for online sources to undestand their meanings.
# import the library for pie chart
# Note: only import matplotlib is nut enough for making piechart
import matplotlib.pyplot as plt
#This definition represents the label of different color parts of the pie chart.
labels = 'USA', 'India', 'Brazil', 'Russia', 'UK'
# This definition represents the size of each part, indicated by numbers, and also useful for calculating the percentage.
sizes = [29862124, 11285561, 11205972, 4360823, 4234924]
# This definition indicates that how long certian parts is away from the main part of pie chart.
explode = (0, 0, 0, 0, 0)
# Combine all indexs together then make a pie chart.
# Note1: shadow indicates the shadow part of the pie chart (if True, then pie chart will have a shadow).
# Note2: startangle indicates the direction of the pie chart (i.e., if values changed, then the pie chart will rotate.).
# Note3: autpct is used to calculated percentage, and also label them on the pie chart.
# Note4: colors is used to change the color of pie chart.
plt.pie(sizes,
explode=explode,
labels=labels,
shadow=False,
startangle=90,
autopct='%1.1F%%',
colors=['C9', 'C2', 'C1', 'C6', 'C4'])
# Make sure that x,y axis have the same scale
plt.axis('equal')
# give this piechart a title
plt.title('Cases of five counties', size=20)
# To show user the pie chart
plt.show()
def bar(x,y,height,corr_hq, corr_lq, incorr_hq, incorr_lq):
p = plt.pie([corr_hq,corr_lq,incorr_hq,incorr_lq]
, colors=["#FFC531","#58D452","#70DB1D","#8AE444"]
, radius=radius)
return mpl.collections.PatchColleckkktion(p)
def main():
driver = webdriver.Chrome('/Users/Taras/bin/chromedriver')
driver.get("https://www.linkedin.com/jobs")
url = driver.current_url
newURL = urllib.parse.unquote(url)
print(newURL)
if newURL != "https://www.linkedin.com/jobs/?trk=jobs-home-jobsfe-redirect":
print("hello")
signIn = urllib.parse.unquote("https://www.linkedin.com/uas/login?session_redirect=https%3A%2F%2Fwww%2Elinkedin%2Ecom%2Fjobs%2F%3Ftrk%3Djobs-home-jobsfe-redirect&fromSignIn=true&trk=uno-reg-join-sign-in")
driver.get(signIn)
email = driver.find_element_by_name("session_key")
email.send_keys("*****@*****.**")
pwd = driver.find_element_by_name("session_password")
pwd.send_keys("Leroyiheanacho19$")
signInBtn = driver.find_element_by_name("signin")
signInBtn.click()
collect(driver)
#logIn = driver.find_element_by_class_name("form-toggle")
#logIn.click()
assert "in" in driver.title
elem = driver.find_element_by_name("keywords")
#elem = driver.find_element_by_xpath("//input[@placeholder='Search jobs by title, keyword or company']")
elem2 = driver.find_element_by_class_name("location-clear-icon")
#elem3 = driver.find_element_by_xpath("//input[@placeholder='City, state, postal code or country']")
elem3 = driver.find_element_by_name("location")
jobSearch = input("What keyword would you like to search for? ")
locSearch = input("Where would you like to run this search on? ")
print("Running a search on the position of " + jobSearch + " in " + locSearch + ".")
#elem.clear()
elem2.click()
elem.send_keys(jobSearch)
elem3.send_keys(locSearch)
elem.send_keys(Keys.RETURN)
#button = driver.find_element_by_xpath("//button[text()='Search']")
#button.click()
assert "No results found." not in driver.page_source
plList = ['Java','C','C++','C#','Python','PHP','Javascript','Visual Basic', 'VB','.NET','Perl','Ruby',
'R','Delphi','Swift','Assembly','Go','Objective-C','PL','SQL','Scratch','Dart','SAS','D','COBOL',
'Ada','Erlang','Lisp','Prolog','LabVIEW', 'HTML','CSS','JQuery','ASP','Groovy','Clojure','Script','Node','Mongo']
linkElements = driver.find_elements_by_class_name("job-title-link")
links = []
for a in linkElements:
linkHref= a.get_attribute("href")
links.append(linkHref)
#print([i for i in links])
#a.send_keys(Keys.RETURN)
#time.sleep(3)
#driver.back()
i=1
frame = pd.DataFrame( columns=[i], index=['Job Title', 'Company', 'Languages', 'Total Langs'])
for link in links:
driver.get(link)
#jobDesc = driver.find_element_by_class_name("description-section")
soup = BeautifulSoup(driver.page_source, "html.parser")
jobDesc = soup.find("div", class_="description-section").text
blob = TextBlob(jobDesc)
#jobTitle1 = urllib.parse.unquote("h1.jobs-details-top-card__job-title.Sans-21px-black-85%-dense")
jobTitle = driver.find_element_by_tag_name("h1").text
companyName = driver.find_element_by_class_name("company").text
companies = []
companies.append(companyName)
print(companies)
#print(jobTitle.text) #works -- prints job title
jobs = []
jobs.append(jobTitle)
print(jobs)
languages = []
#results = soup.p.find_all(string=re.compile('.*{0}.*'.format(plList)), recursive=True)
#print(results)
count = 0
for lang in plList:
if lang not in blob:
continue
elif lang in blob:
languages.append(lang)
count = count + 1
jobs = ''.join(jobs)
companies = ''.join(companies)
languages = ', '.join(languages)
finalArr = []
finalArr.append(jobs)
finalArr.append(companies)
finalArr.append(languages)
finalArr.append(count)
frame[i] = finalArr
print(frame)
i = i + 1
colors = ["#E13F29", "#D69A80", "#D63B59", "#AE5552", "#CB5C3B", "#EB8076", "#96624E"]
# Create a pie chart
plt.pie(
# using data total)arrests
df['Total Langs'],
# with the labels being officer names
labels=df['Languages'],
# with no shadows
shadow=False,
# with colors
colors=colors,
# with one slide exploded out
explode=(0, 0, 0, 0, 0.15),
# with the start angle at 90%
startangle=90,
# with the percent listed as a fraction
autopct='%1.1f%%',
)
# View the plot drop above
plt.axis('equal')
# View the plot
plt.tight_layout()
plt.show()
x = np.array(["A", "B", "C", "D"]) y = np.array([3, 8, 1, 10]) # plt.bar(x, y, color = "hotpink") # plt.show() # Matplotlib Bars x = np.random.normal(170, 10, 250) print(x) plt.hist(x) plt.show() # Matplotlib Pie Charts y = np.array([55, 15, 25, 10]) plt.pie(y) plt.show() # Labels y = np.array([55, 15, 25, 10]) mylabels = ["Apples", "Bananas", "Cherries", "Dates"] plt.pie(y, labels=mylabels) plt.show() # Start Angle y = np.array([55, 15, 25, 10]) mylabels = ["Apples", "Bananas", "Cherries", "Dates"] plt.pie(y, labels=mylabels, startangle=90) plt.show() # Explode
categorical = ["target",
"sex",
"cp",
"fbs",
"restecg",
"exang",
"slope",
"ca",
"thal"]
plt.figure(figsize=(14,20))
for i in range(1,10):
labels = data[categorical[i-1]].value_counts().index
sizes = data[categorical[i-1]].value_counts().values
plt.subplot(5,2,i)
plt.pie(sizes, labels=labels, autopct='%1.1f%%',
shadow=True, startangle=90)
plt.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
plt.xticks([])
plt.yticks([])
plt.title(categorical[i-1].upper())
plt.show()
plt.figure(figsize=(10,17))
plt.subplot(5,2,1)
sns.kdeplot(data.loc[data["target"]==1]["age"],color="green",shade=True)
sns.kdeplot(data.loc[data["target"]==0]["age"],color="red",shade=True)
plt.legend(["target:1","target:0"])
plt.title("Age".upper())
for i in range(2,9):
plt.subplot(5,2,i)
import matplotlib as mpl
from matplotlib.gridspec import GridSpec
import matplotlib.pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')
# In[23]:
labels = list(data1['AgeGroup'])
sizes = list(data1['TotalCases'])
explode = []
for i in labels:
explode.append(0.05)
plt.figure(figsize= (15,10))
plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=9, explode =explode)
centre_circle = plt.Circle((0,0),0.70,fc='white')
fig = plt.gcf()
fig.gca().add_artist(centre_circle)
plt.title('India - Age Group wise Distribution',fontsize = 20)
plt.axis('equal')
plt.tight_layout()
# ## Importing and Reading the Dataset
# HospitalBedsIndia.csv
# In[24]:
data2 = pd.read_csv(r"D:\DATA SCIENCE\Covid19 analysis\557629_1323860_bundle_archive\HospitalBedsIndia.csv")
# Plot Pie Chart for Year 2011 Country Wise.
import pandas as pd
import matplotlib as plt
df = pd.read_csv("BigMartSalesData.csv")
df_grouped_country = df.query("Year == 2011").filter(
["Country", "Amount"]).groupby(["Country"], as_index=False).sum()
plt.pie(df_grouped_country["Amount"],
labels=df_grouped_country["Country"],
autopct='%1.1f%%',
shadow=True)
plt.tight_layout()
plt.show()
# Which Country contributes highest towards sales?
# United Kingdom
import matplotlib as plt
labels = 'Python', 'C++', 'Ruby', 'Java', 'PHP', 'Pearl'
sizes = {33,52,12,17,62,48}
seperated = {.1,0,0,0,0,0}
plt.pie(sizes, labels = labels, autopct='%1.1f%%', explode=seperated)
plt.axis('equal')
plt.show()
# In[13]:
#ploting piechart
fig = plt.figure(figsize=(15,12))
plt.suptitle('Pie Chart Distributions', fontsize=20)
for i in range(1,dataset2.shape[1]+1):
plt.subplot(6,3,i)
f=plt.gca()
f.axes.get_yaxis().set_visible(False)
f.set_title(dataset2.columns.values[i-1])
values=dataset2.iloc[:,i-1].value_counts(normalize=True).values
index=dataset2.iloc[:,i-1].value_counts(normalize=True).index
plt.pie(values,labels=index,autopct='%1.1f%%')
plt.tight_layout(rect=[0,0.03,1,0.95])
# In[14]:
## Exploring Uneven Features
dataset[dataset2.waiting_4_loan==1].churn.value_counts()
# In[15]:
dataset[dataset2.cancelled_loan == 1].churn.value_counts()
msg = "Do you want to continue?"
title = "Please Confirm"
if choice=="Pie": # show a Continue/Cancel dialog
pass# user chose Continue
import numpy as np
import matplotlib as plt
import matplotlib.pyplot as plt
%matplotlib inline
labels='Red','Green','Yellow','Blue'
x=int(input("Enter a value"))
y=int(input("Enter a value"))
z=int(input("Enter a value"))
w=int(input("Enter a value"))
sizes=[x,y,z,w]
colours=['Red','Green','Yellow','Blue']
plt.pie(sizes,labels=labels)
plt.axis('equal')
plt.show()
sys.exit(0)
elif choice=="Line Graph":
import matplotlib.pyplot as plt
x = [1,2,3]
y = [5,7,4]
x2 = [1,2,3]
y2 = [10,14,12]
plt.plot(x,y,label='First Line')
plt.plot(x2,y2,label='Second Line')
plt.xlabel('Plot Number')
plt.ylabel('Important Var')
print(restaurants.head())
cuisine_options_count = restaurants.cuisine.nunique()
cuisine_counts = restaurants.groupby('cuisine').id.count().reset_index()
restaurants = pd.read_csv('restaurants.csv')
cuisine_counts = restaurants.groupby('cuisine')\
.name.count()\
.reset_index()
cuisines = cuisine_counts.cuisine.values
counts = cuisine_counts.name.values
plt.pie(counts, labels=cuisines, autopct='%d%%')
plt.title('FoodWheel')
plt.axis('equal')
plt.show()
orders = pd.read_csv('orders.csv')
print(orders.head())
month = lambda date: date.split('-')[0]
orders['month'] = orders.date.apply(month)
avg_order = orders.groupby('month').price.mean().reset_index()
std_order = orders.groupby('month').price.std().reset_index()
orders['month'] = orders.date.apply(lambda x: x.split('-')[0])
#grade b
grade_b = np.sum((student[:, 31] > 80) & (student[:, 31] < 90))
print("Number of students with grade B: ")
print(grade_b)
#grade c
grade_c = np.sum((student[:, 31] > 70) & (student[:, 31] < 80))
print("Number of students with grade C: ")
print(grade_c)
#grade d
grade_d = np.sum((student[:, 31] > 60) & (student[:, 31] < 70))
print("Number of students with grade D: ")
print(grade_d)
#grade f
grade_f = student[:, 31] <= 59
print("Number of students with grade F: ")
print(student[grade_f, :].shape[0])
#create pie chart
Grades = [21, 8, 1, 1, 2]
slice_labels = [
'Grade is A', ' Grade is B', 'Grade is C', 'Grade is D', 'Grade is F'
]
explode = (0.1, 0, 0, 0, 0) #only explode A grade
plt.pie(Grades, labels=slice_labels, explode=explode)
plt.title('Student Grades')
plt.show()
