<JUPYTER>

analysis-header-start.py

import os, pickle, gc, json
import datetime as dt
from pprint import pprint
from tqdm import tqdm_notebook as tqdm

from funcy import *
import numpy as np
import pandas as pd
import pandas_profiling

import matplotlib.pyplot as plt
import seaborn as sns
from plotnine import *

%matplotlib inline
%config InlineBackend.figure_format = 'retina'
%load_ext autoreload
%autoreload 2

#import warnings; warnings.filterwarnings("ignore")

analysis-header.py

# optional formatting changes
pd.set_option("display.precision", 2)
pd.set_option("display.float_format", '{:20,.2f}'.format)
np.set_printoptions(suppress=True)
pd.set_option('display.max_columns', 200)

# plot styles
plt.style.use('seaborn')
sns.set(style='ticks') # whitegrid for lines
plt.rcParams['figure.figsize'] = (10.0, 6.0)

holoviews.py

import hvplot.pandas
import holoviews as hv
from holoviews import opts
hv.extension('bokeh')

pickle.py

data_dir = partial(os.path.join, '/media/user/Data/ML/mlcourse.ai/data/a10/')

def pickle_it(obj, filename):
    with open(data_dir(filename), 'wb') as f:
        pickle.dump(obj, f, protocol=2)

def unpickle(filename):
    with open(data_dir(filename), 'rb') as f:
        return pickle.load(f)

plotly.py

from plotly.offline import iplot, init_notebook_mode
init_notebook_mode(connected=False)

vim.py

import os, pickle, gc, json
from pprint import pprint
from tqdm import tqdm

from funcy import *

import pandas as pd
import numpy as np
import datetime as dt

pd.set_option("display.precision", 2)
pd.set_option("display.float_format", '{:20,.2f}'.format)
np.set_printoptions(suppress=True)
pd.set_option('display.max_columns', 200)

import matplotlib.pyplot as plt
import seaborn as sns

plt.style.use('seaborn')
sns.set(style='ticks') # whitegrid for lines
plt.rcParams['figure.figsize'] = (15.0, 9.0)


import plotnine
plotnine.options.figure_size = (15, 9)
from plotnine import *