It's recommended that you install the requirements for these t5 models in a new environment since they are known to conflict with common Python package requirements in the scientific Python Stack.
conda create -n nlp-t5
import finplot as fplt | |
import yfinance | |
df = yfinance.download('AAPL') | |
d = df[['Open', 'Close', 'High', 'Low']].reset_index(drop=True) | |
fplt.candlestick_ochl(d) | |
fplt.show() |
--- eventloops.py-orig 2020-12-12 10:22:08.633178000 -0800 | |
+++ eventloops.py 2020-12-12 10:22:56.335735100 -0800 | |
@@ -20,7 +20,7 @@ | |
Checks if we are on OS X 10.9 or greater. | |
""" | |
- return sys.platform == 'darwin' and V(platform.mac_ver()[0]) >= V('10.9') | |
+ return sys.platform == 'darwin' and V(platform.mac_ver()[0]) >= V('10.9') and platform.mac_ver()[2] != 'arm64' | |
## import any sklearn models and collect predictions / probabilities beforehand | |
import matplotlib.pyplot as plt | |
from cycler import cycler | |
## Line color config -- rather than create a structure with a finite color palette, use your own to cycle through a list. | |
default_cycler = (cycler(color=['r', 'g', 'b', 'y']) + | |
cycler(linestyle=['-', '--', ':', '-.'])) | |
plt.rc('axes', prop_cycle = default_cycler) |
def fetch_states(): | |
""" | |
Return all <option> values for the <select> element with all states. | |
""" | |
data = {} | |
states = tree.xpath('//select[@name="state"]') | |
try: | |
for state in states[0].xpath('option'): | |
data[state.attrib['value']] = state.text_content() | |
except: |
import requests, re | |
def test_station_data_availability(station_id): | |
for year in range(1960, 2020 + 1): | |
r = requests.get(f"https://www.ncei.noaa.gov/data/local-climatological-data/access/{year}/") | |
matches = re.search(r"href=\"([0-9]{6}" +str(station_id) + ".csv)", r.text) | |
if matches: | |
print(station_id, " data exists for ", year) | |
else: | |
print(station_id, " data not found for ", year) |
import pandas as pd | |
import numpy as np | |
from sklearn.datasets import load_wine | |
# Load example wine dataset from sklearn | |
data = load_wine() | |
# Create a basic DataFrame | |
df = pd.DataFrame(data['data'], columns = data['feature_names']) |