def test_wdi_search(self):
# Test that a name column exists, and that some results were returned
# ...without being too strict about what the actual contents of the
# results actually are. The fact that there are some, is good enough.
result = search('gdp.*capita.*constant')
self.assertTrue(result.name.str.contains('GDP').any())
async def gdp(ctx, *, message):
matches = wb.search('gdp.*capita.*const')
dat = wb.download(indicator='NY.GDP.MKTP.CD',
country=[message],
start=1999,
end=2030)
gdp = dat / 1000000000000
await ctx.send(f'**{message} GDP** in USD trillions```{gdp}```')
def test_wdi_search(self):
# Test that a name column exists, and that some results were returned
# ...without being too strict about what the actual contents of the
# results actually are. The fact that there are some, is good enough.
result = search('gdp.*capita.*constant')
self.assertTrue(result.name.str.contains('GDP').any())
def chart2(indicator1, countryCode, startY, endY):
import matplotlib
from io import BytesIO
import base64
import pandas as pd
pd.core.common.is_list_like = pd.api.types.is_list_like
from pandas_datareader import wb
matplotlib.use("agg")
import matplotlib.pyplot as plt
# mathces = wb.search('gdp.*capita.*const')
# import wbdata
mathces = wb.search('gni.*capita.*const')
# set up the countries I want
# countries = ['US', 'CA', 'MX']
# set up the indicator I want (just build up the dict if you want more than one)
# indicators = {'NY.GNP.PCAP.CD':'GNI per Capita'}
# grab indicators above for countires above and load into data frame
id = indicator1
ct = countryCode
st = startY
ed = endY
df = wb.download(indicator=id, country=ct, start=st, end=ed)
# df is "pivoted", pandas' unstack fucntion helps reshape it into something plottable
dfu = df.unstack(level=0)
# a simple matplotlib plot with legend, labels and a title
dfu.plot()
plt.legend(loc='best')
plt.title("Result")
plt.xlabel('Date')
plt.ylabel('Indicator1')
sio = BytesIO()
plt.savefig(sio, format='png')
data = base64.encodebytes(sio.getvalue()).decode()
print(data)
html = '''
<html>
<body>
<img src="data:image/png;base64,{}" />
</body>
<html>
'''
picture = "data:image/png;base64," + data
# print(picture)
plt.close()
# return html.format(data)
return render_template("home.html", picture=picture)
def test_wdi_search(self):
# Test that a name column exists, and that some results were returned
# ...without being too strict about what the actual contents of the
# results actually are. The fact that there are some, is good enough.
result = search('gdp.*capita.*constant')
self.assertTrue(result.name.str.contains('GDP').any())
# check cache returns the results within 0.5 sec
current_time = time.time()
result = search('gdp.*capita.*constant')
self.assertTrue(result.name.str.contains('GDP').any())
self.assertTrue(time.time() - current_time < 0.5)
result2 = WorldBankReader().search('gdp.*capita.*constant')
session = requests.Session()
result3 = search('gdp.*capita.*constant', session=session)
result4 = WorldBankReader(session=session).search('gdp.*capita.*constant')
for result in [result2, result3, result4]:
self.assertTrue(result.name.str.contains('GDP').any())
def test_wdi_search(self):
# Test that a name column exists, and that some results were returned
# ...without being too strict about what the actual contents of the
# results actually are. The fact that there are some, is good enough.
result = search('gdp.*capita.*constant')
self.assertTrue(result.name.str.contains('GDP').any())
# check cache returns the results within 0.5 sec
current_time = time.time()
result = search('gdp.*capita.*constant')
self.assertTrue(result.name.str.contains('GDP').any())
self.assertTrue(time.time() - current_time < 0.5)
result2 = WorldBankReader().search('gdp.*capita.*constant')
session = requests.Session()
result3 = search('gdp.*capita.*constant', session=session)
result4 = WorldBankReader(session=session).search('gdp.*capita.*constant')
for result in [result2, result3, result4]:
self.assertTrue(result.name.str.contains('GDP').any())
def get_data_id(self, search_keywords):
"""
:param search_keywords: list
:return: list
"""
search_keywords = search_keywords.split()
try:
data = wb.search('|'.join(search_keywords))
self.data = data
return data['name'].to_list()
except Exception as error:
self.current_error = error
self.data = pd.DataFrame()
return pd.DataFrame()
from pandas_datareader import wb
import matplotlib.pyplot as plt
mathces = wb.search('gni.*capita.*const')
#grab indicator,country, period I want and load into data frame
df = wb.download(indicator='NY.GNP.PCAP.CD', country=['CL', 'UY', 'HU'], start=1990, end=2010)
#df is "pivoted", pandas' unstack fucntion helps reshape it into something plottable
dfu = df.unstack(level=0)
# a simple matplotlib plot with legend, labels and a title
dfu.plot();
plt.legend(loc='best');
plt.title("GNI Per Capita ($USD, Atlas Method)");
plt.xlabel('Date'); plt.ylabel('GNI Per Capita ($USD, Atlas Method');
plt.show();
def bpress():
global feat
global pos
text=e.get()
feat=feat.append(wb.search(text,field='name',case=False))
"""
Author: Самаркин
Date: 27/10/2019
Purpose: test
"""
# стандартная секция импорта, будет использоваться всегда
# модули числовых расчетов
import scipy as sc
import numpy as np
import pandas as pd
# модуль библиотеки для вывода графиков в стиле Matlab
import matplotlib
matplotlib.use(
'Qt5Agg'
) # библиотека для вывода графиков на базе Qt5, нужен импорт модуля PyQt5
# модуль печати плоских графиков
import matplotlib.pyplot as plt
plt.style.use('bmh') # можно попробовать также 'ggplot' или 'classic'
fig = plt.figure(
) # поверхность для вывода графиков (в виде всплывающего окна)
from pandas_datareader import wb
matches = wb.search('gdp.*capita.*const')
dat = wb.download(indicator='NY.GDP.PCAP.KD', start=2005, end=2008)
print(dat)
plt.show()
def search_wb(query):
return wb.search(query)[["id","name","source"]]
def search_wb(query):
return wb.search(query)[["id", "name", "source"]]
print(inflation.info())
# Fama/French
from pandas_datareader.famafrench import get_available_datasets
print(get_available_datasets())
ds = web.DataReader('5_Industry_Portfolios', 'famafrench')
print(ds['DESCR'])
# World Bank
from pandas_datareader import wb
gdp_variables = wb.search('gdp.*capita.*const')
gdp_variables.head()
wb_data = wb.download(indicator='NY.GDP.PCAP.KD',
country=['US', 'CA', 'MX'],
start=1990,
end=2019)
print(wb_data.head())
# OECD
df = web.DataReader('TUD', 'oecd', end='2015')
print(df[['Japan', 'United States']])
# EuroStat
import pandas as pd
import requests
# Specific imports
import pandas_datareader as pdr
from pandas_datareader import wb
# In[28]:
# This link proved very useful :
# https://pydata.github.io/pandas-datareader/stable/remote_data.html#world-bank
# In[71]:
# Search in the available indicators which on matches our search
matches = wb.search('Statistical')
matches[['id', 'name', 'sourceNote']].values
# Seems like the 2nd indicator is the one we are interested in
# In[73]:
MY_IND = 'IQ.SCI.OVRL'
START_YEAR = 2010
matches = wb.download(indicator=MY_IND,
country='all',
start=START_YEAR,
end=2019)
# In[74]:
symbol = 'AAPL'
starttime = datetime.datetime(2015, 1, 1)
endtime = datetime.datetime(2018, 12, 31)
apple = pdr.get_data_yahoo(symbol, starttime, endtime)
type(apple)
# <class 'pandas.core.frame.DataFrame'>
apple.to_csv('apple.csv')
symbol2 = '^GSPC'
starttime2 = datetime.datetime(2010, 1, 1)
endtime2 = datetime.datetime(2019, 1, 7)
sp500 = pdr.get_data_yahoo(symbol2, starttime2, endtime2)
sp500.to_csv('sp500.csv')
# for world bank dataset
matches = wb.search('gdp.*capita.*const')
data = wb.download(indicator='NY.GDP.PCAP.KD',
country=['US', 'CA', 'MX'],
start=2005,
end=2008)
data.head()
data['NY.GDP.PCAP.KD'].groupby(level=0).mean()
wb.search('cell.*%').iloc[:, :2]
ind = ['NY.GDP.PCAP.KD', 'IT.MOB.COV.ZS']
ind = ['NY.GDP.PCAP.KD', 'IT.MOB.COV.ZS']
Idat = wb.download(indicator=ind, country='all', start=2011, end=2011).dropna()
Idat.columns = ['gdp', 'cellphone']
print(Idat.head())
print(Idat.tail())
"""
INTL 550 - HW2 (Data collection)
Name : Shukhrat Khuseynov
ID : 0070495
"""
import numpy as np
import pandas as pd
from pandas_datareader import wb # World Bank API from pandas_datareader
matches1 = wb.search('life expectancy') # choosing SP.DYN.LE00.IN
matches2 = wb.search('gdp per capita') # choosing NY.GDP.PCAP.CD
matches3 = wb.search('school enrollment') # choosing SE.PRE.ENRR
matches4 = wb.search('birth rate') # choosing SP.DYN.CBRT.IN
# obtaining all indicators at once
df = wb.download(indicator=[
'SP.DYN.LE00.IN', 'NY.GDP.PCAP.CD', 'SE.PRE.ENRR', 'SP.DYN.CBRT.IN'
],
country='TJ',
start=1990,
end=2020)
# formatting the table
df.columns = ['lifexp', 'gdp', 'school', 'birth']
df = df[::-1] # reverting the data
df = df.dropna()
print(df)
import pandas_datareader.wb as wb data = wb.search(string='SE.PRM.TENR', field='id') data
def world_bank():
from pandas_datareader import wb
gdp_variables = wb.search('gdp.*capita.*const')
gdp_variables.head()
"""
Created on Fri Nov 10 19:11:36 2017
@author: mmic
"""
import pandas as pd
import numpy as np
import datetime
import matplotlib.pyplot as plt
import pandas_datareader.data as web
from pandas_datareader import wb
## Searching variables with the key word CO2
dat=wb.search('CO2').iloc[:,:2]
print(dat)
# Importing Data from World Bank API
var = ['EN.ATM.GHGT.KT.CE','EN.CO2.TRAN.ZS','EN.CO2.BLDG.ZS',
'EN.CO2.MANF.ZS','EN.CO2.OTHX.ZS','EN.CO2.ETOT.ZS']
con = ['WLD','USA','CHN','IND','JPN','RUS']
st=1960
en=2016
df= wb.download(indicator=var, country=con, start=st, end=en).dropna()
# Renaming the variable names
df=df.rename(columns={'EN.ATM.GHGT.KT.CE':'TE'})
# Graph 1
df1=df.unstack(level=0)
# Japan 2014 127276000
# 2013 127445000
# 2012 127629000
# 2011 127833000
# 2010 128070000
# ... ...
# United States 1989 246819000
# 1988 244499000
# 1987 242289000
# ... ...
# 1962 186538000
# 1961 183691000
# 1960 180671000
# [110 rows x 1 columns]
print(wb.search('gdp.*capita.*const').iloc[:, :2])
# id name
# 685 6.0.GDPpc_constant GDP per capita, PPP (constant 2011 internation...
# 8086 NY.GDP.PCAP.KD GDP per capita (constant 2010 US$)
# 8088 NY.GDP.PCAP.KN GDP per capita (constant LCU)
# 8090 NY.GDP.PCAP.PP.KD GDP per capita, PPP (constant 2011 internation...
# 8091 NY.GDP.PCAP.PP.KD.87 GDP per capita, PPP (constant 1987 internation...
f = wb.download(indicator='SP.POP.TOTL',
country=['JP', 'US'],
start=1960,
end=2014)
f2 = f.unstack(level=0)
print(f2)
# SP.POP.TOTL
# country Japan United States
