liquidgenius

Amazon Rekognition - Python Code Samples

1. Labels Detection
2. Faces Detection
3. Faces Comparison
4. Faces Indexing
5. Faces Search

liquidgenius

# List unique values in a DataFrame column
# h/t @makmanalp for the updated syntax!
df['Column Name'].unique()

# Convert Series datatype to numeric (will error if column has non-numeric values)
# h/t @makmanalp
pd.to_numeric(df['Column Name'])

# Convert Series datatype to numeric, changing non-numeric values to NaN
# h/t @makmanalp for the updated syntax!

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# Compiled source #
###################
*.com
*.class
*.dll
*.exe
*.o
*.so

# Packages #

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# uses the date_to_milliseconds and interval_to_milliseconds functions
# https://gist.github.com/sammchardy/3547cfab1faf78e385b3fcb83ad86395
# https://gist.github.com/sammchardy/fcbb2b836d1f694f39bddd569d1c16fe

from binance.client import Client
import time

def get_historical_klines(symbol, interval, start_str, end_str=None):
    """Get Historical Klines from Binance

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def interval_to_milliseconds(interval):
    """Convert a Binance interval string to milliseconds

    :param interval: Binance interval string 1m, 3m, 5m, 15m, 30m, 1h, 2h, 4h, 6h, 8h, 12h, 1d, 3d, 1w
    :type interval: str

    :return:
         None if unit not one of m, h, d or w
         None if string not in correct format
         int value of interval in milliseconds

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import dataset
import pandas as pd

# create dataframe
df = pd.DataFrame()
names = ['Bob', 'Jane', 'Alice', 'Ricky']
ages = [31, 30, 31, 30]
df['names'] = names
df['ages'] = ages

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https://github.com/Jamonek/Robinhood

def depaginate (rh_data):

    res = rh_data['results']

    while rh_data['next'] not in ['null', None]:

        rh_data = rh.get_url(rh_data['next'])