yassineAlouini

import pandas as pd
import matplotlib.pylab as plt
import matplotlib.dates as mdates
hours = mdates.HourLocator(interval = 1)
h_d_fmt = mdates.DateFormatter('%d-%m %H:%M:%S')

DATA_PATH = "/path/to/your/data"
TMS_COL = "timestamp_column"
COL_TO_PLOT = "column_to_plot"

yassineAlouini

1. Update the version in the setup.py file.

2. Run: python3 setup.py sdist bdist_wheel.

3. Run the following: python setup.py sdist upload -r pypi.

Notice that this last command is deprecated. Use twine instead.

yassineAlouini

# -*- coding: utf-8 -*-


from functools import wraps
import numpy as np
from shapely import wkb


def vectorize(**vectorize_kwargs):
    """ A (numpy) vectorizing decorator

yassineAlouini

# Inspired from here: https://stackoverflow.com/questions/17095101/outputting-difference-in-two-pandas-dataframes-side-by-side-highlighting-the-d

def _show_dfs_difference(df1, df2):
    ne_stacked = (df1 != df2).stack()
    difference_locations = np.where(df1 != df2)
    changed = ne_stacked[ne_stacked]
    changed_from = df1.values[difference_locations]
    changed_to = df2.values[difference_locations]
    return pd.DataFrame({'from': changed_from, 'to': changed_to}, index=changed.index)

yassineAlouini

from shapely import wkb
import numpy as np
from functools import wraps

def vectorize(**vectorize_kwargs):
    """ A (numpy) vectorizing decorator
    """
    def _vectorize(func):
        @wraps(func)
        def wrapper(*args, **kwargs):

yassineAlouini

import pandas as pd
import itertools
import  numpy as np


# From here: https://pandas.pydata.org/pandas-docs/stable/cookbook.html#creating-example-data
def expand_grid(data_dict):
  rows = itertools.product(*data_dict.values())
  return pd.DataFrame.from_records(rows, columns=data_dict.keys())

yassineAlouini

import geopandas as gpd
import numpy as np
# Set a seed for reproducibility
np.random.seed(0)

input_path = 'input/path/to/geojson'
output_path = 'output/path/to/geojson'

choices = [1, 2, 3]

yassineAlouini

import pandas as pd
df = pd.DataFrame({'t': [1, 2, 3, 4, 5, 6], 'sid': [1, 1, 1, 2, 2, 2], 'time': [1, 2, 2, 2, 1, 1]})
hist_df = df.groupby(['sid', 'time']).t.value_counts(bins=[1, 2, 3, 10]).reset_index(name='counts')

yassineAlouini

import elo
import pandas as pd
from slacker import Slacker
from tabulate import tabulate
import emoji

slack = Slacker('SLACK_API_KEY')  # API key
test = False
game = True
K_FACTOR = 40

yassineAlouini

# Inspired from here: https://bokeh.pydata.org/en/latest/docs/user_guide/geo.html

from bokeh.tile_providers import CARTODBPOSITRON
from bokeh.io import output_file, show
from bokeh.plotting import figure
bound = 20000000 # meters
fig = figure(tools='pan, wheel_zoom', x_range=(-bound, bound), y_range=(-bound, bound))
fig.axis.visible = False
fig.add_tile(CARTODBPOSITRON)
show(fig)