我正在尝试创建一个循环，它将返回每个自动收报机， 1.一个不同的数据框（通过股票代码的名称） 2.将时间列转换为＆＃34;正常＆＃34;天 3.它（新时间）将用作该数据框的索引。
如果我为每个自动收报机运行它，它的工作没有问题。 感谢您的帮助！
import requests
import pandas as pd
desired_width = 320
pd.set_option('display.width', desired_width)
data = pd.DataFrame()
tickers = ['BTC', 'ETH', 'XRP'] # pools of tickers to get
for t in tickers: # a loop to get data ticker by ticker
url = 'https://min-api.cryptocompare.com/data/histoday' + \
'?fsym=' + \
t +\
'&tsym=USD' + \
'&limit=600000000000' + \
'&aggregate=1' + \
'&e=CCCAGG'
response = requests.get(url)
data[t] = response.json()['Data']
#the following 2 lines I failed to execute
#data[t]['time'] = pd.to_datetime(data[t]['time'], unit='s')
#data[t].index = data[t]['time']
print("downloading data for: " + t)
print("data for:" + t, data.head(5))
我的结果是所有三个代码的一个数据框：
数据：XRP BTC
ETH XRP 0 {＆＃39;时间＆＃39;： 1342742400，＆＃39;关闭＆＃39;：8.52，＆＃39;高＆＃39;：8 .... {＆＃39;时间＆＃39;：1342742400， ＆＃39;关闭＆＃39;：0，＆＃39;高＆＃39;：0，＆＃39; l ... {＆＃39;时间＆＃39;：1342742400，＆＃39;关闭＆＃39; ：0，＆＃39;高＆＃39;： 0，＆＃39; l ... 1 {＆＃39;时间＆＃39;：1342828800，＆＃39;关闭＆＃39;：8.85，＆＃39;高＆＃39;：9 .... {＆＃39;时间＆＃39;：1342828800，＆＃39;关闭＆＃39;：0，＆＃39;高＆＃39;：0，＆＃39; l ... {＆＃39;时间＆＃39 ;： 1342828800，＆＃39;关闭＆＃39;：0，＆＃39;高＆＃39;：0，＆＃39; l ... 2 {＆＃39;时间＆＃39;：1342915200， ＆＃39;关闭＆＃39;：8.41，＆＃39;高＆＃39;：8 .... {＆＃39;时间＆＃39;：1342915200，＆＃39;关闭＆＃39;：0，＆＃ 39;高＆＃39 ;: 0，＆＃39; l ... {＆＃39;时间＆＃39;：1342915200，＆＃39;关闭＆＃39;：0，＆＃39;高＆＃39;：0，＆＃39; l。 .. 3 {＆＃39;时间＆＃39;：1343001600，＆＃39;关闭＆＃39;：8.45，＆＃39;高＆＃39;：9 .... {＆＃39;时间＆＃39;： 1343001600，＆＃39;关闭＆＃39;：0，＆＃39;高＆＃39;：0，＆＃39; l ... {＆＃39;时间＆＃39;：1343001600， ＆＃39;关闭＆＃39;：0，＆＃39;高＆＃39;：0，＆＃39; l ... 4 {＆＃39;时间＆＃39;：1343088000，＆＃39;关闭＆＃39 ;：8.6， ＆＃39;高＆＃39;：8.8 ... {＆＃39;时间＆＃39;：1343088000，＆＃39;关闭＆＃39;：0，＆＃39;高＆＃39;：0，＆＃39 ; 1 ... {＆＃39;时间＆＃39;：1343088000，＆＃39;关闭＆＃39;：0，＆＃39;高＆＃39;：0，＆＃39; l ...
我在Windows 10上使用python 3.6和pycharm + anconda
答案 0 :(得分：1)
我认为dinctionary of DataFrame对DataFrame MultiIndex tickers from pandas.io.json import json_normalize
data = {}
tickers = ['BTC', 'ETH', 'XRP'] # pools of tickers to get
for t in tickers: # a loop to get data ticker by ticker
url = 'https://min-api.cryptocompare.com/data/histoday' + \
'?fsym=' + \
t +\
'&tsym=USD' + \
'&limit=600000000000' + \
'&aggregate=1' + \
'&e=CCCAGG'
response = requests.get(url)
data[t] = json_normalize(response.json()['Data'])
df = pd.concat(data)
print (df.head())
close high low open time volumefrom volumeto
BTC 0 8.52 8.87 7.60 8.87 1342742400 154661.12 1267523.74
1 8.85 9.70 7.96 8.52 1342828800 139906.90 1242153.88
2 8.41 8.97 8.27 8.85 1342915200 30070.67 259113.81
3 8.45 9.20 7.75 8.41 1343001600 146396.18 1238579.49
4 8.60 8.85 8.34 8.45 1343088000 40946.86 353506.54
与print (df.xs('BTC').head())
#print (df.loc['BTC'].head())
close high low open time volumefrom volumeto
0 8.52 8.87 7.60 8.87 1342742400 154661.12 1267523.74
1 8.85 9.70 7.96 8.52 1342828800 139906.90 1242153.88
2 8.41 8.97 8.27 8.85 1342915200 30070.67 259113.81
3 8.45 9.20 7.75 8.41 1343001600 146396.18 1238579.49
4 8.60 8.85 8.34 8.45 1343088000 40946.86 353506.54
使用json_normalize - 第一级为concat：
dictionary然后选择每个级别是否可用：
data = {}
tickers = ['BTC', 'ETH', 'XRP'] # pools of tickers to get
for t in tickers: # a loop to get data ticker by ticker
url = 'https://min-api.cryptocompare.com/data/histoday' + \
'?fsym=' + \
t +\
'&tsym=USD' + \
'&limit=600000000000' + \
'&aggregate=1' + \
'&e=CCCAGG'
response = requests.get(url)
data[t] = json_normalize(response.json()['Data'])
data[t] = data[t].set_index(pd.to_datetime(data[t]['time'], unit='s'))
print (data['BTC'].head())
close high low open time volumefrom volumeto
time
2012-07-20 8.52 8.87 7.60 8.87 1342742400 154661.12 1267523.74
2012-07-21 8.85 9.70 7.96 8.52 1342828800 139906.90 1242153.88
2012-07-22 8.41 8.97 8.27 8.85 1342915200 30070.67 259113.81
2012-07-23 8.45 9.20 7.75 8.41 1343001600 146396.18 1238579.49
2012-07-24 8.60 8.85 8.34 8.45 1343088000 40946.86 353506.54
另一种方法不是data = {}
tickers = ['BTC', 'ETH', 'XRP'] # pools of tickers to get
for t in tickers: # a loop to get data ticker by ticker
url = 'https://min-api.cryptocompare.com/data/histoday' + \
'?fsym=' + \
t +\
'&tsym=USD' + \
'&limit=600000000000' + \
'&aggregate=1' + \
'&e=CCCAGG'
response = requests.get(url)
globals()['df_' + str(t)] = json_normalize(response.json()['Data'])
globals()['df_' + str(t)] = globals()['df_' + str(t)].set_index(pd.to_datetime(globals()['df_' + str(t)]['time'], unit='s'))
print (df_BTC.head())
close high low open time volumefrom volumeto
time
2012-07-20 8.52 8.87 7.60 8.87 1342742400 154661.12 1267523.74
2012-07-21 8.85 9.70 7.96 8.52 1342828800 139906.90 1242153.88
2012-07-22 8.41 8.97 8.27 8.85 1342915200 30070.67 259113.81
2012-07-23 8.45 9.20 7.75 8.41 1343001600 146396.18 1238579.49
2012-07-24 8.60 8.85 8.34 8.45 1343088000 40946.86 353506.54
，只创建int foo (std::string, size_t = 0) { return 42; }
：
template <typename T>
auto boo (std::string s) {
return (*T::value) (s);
}
编辑：如果想要全局变量不推荐解决方案：
T答案 1 :(得分：0)
我管理定义了一个函数来获取数据，而不是使用循环来获取所有的代码。这解决了这个问题。
import requests
import datetime
import pandas as pd
import matplotlib.pyplot as plt
desired_width = 320
pd.set_option('display.width', desired_width)
#function to download the Historical HOUR data
def hourly_price_historical(symbol, comparison_symbol, limit, aggregate, exchange=''):
url = 'https://min-api.cryptocompare.com/data/histohour?fsym={}&tsym={}&limit={}&aggregate={}'\
.format(symbol.upper(), comparison_symbol.upper(), limit, aggregate)
if exchange:
url += '&e={}'.format(exchange)
page = requests.get(url)
data = page.json()['Data']
df = pd.DataFrame(data)
df['timestamp'] = [datetime.datetime.fromtimestamp(d) for d in df.time]
df = df.drop('time', 1)
df.set_index('timestamp')
return df
data = {}
tickers = ['BTC', 'ETH', 'XRP'] # pools of tickers to get
for t in tickers: # a loop to get data ticker by ticker
data[t] = hour_data = hourly_price_historical(t,'USD', 9999999,1) # calling the function defined above
print("Getting the data for: ", t)
globals()['df_' + str(t)] = data