Ailuropoda1864

import subprocess
import os
import time
import smtplib
from email.message import EmailMessage


# put your GA directory here, e.g. '/home/fay/code/GA/DSI'
CWD = ''

Ailuropoda1864

import csv


def append_to_csv(csvfile, entry):
    """
    append a row to a csv file
    :param csvfile: the path to a .csv file
    :param entry: a list representing a row in the .csv file
    :return: None
    """

Ailuropoda1864

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from functools import partial


def corr_heatmap(dataframe, cmap=sns.diverging_palette(220, 10, as_cmap=True),
                 **kwargs):
    """

Ailuropoda1864

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import scipy.stats as stats


def scatter_plot_with_linear_fit(x, y, slope=None, y_intercept=None):
    """
    :param x: an array
    :param y: an array

Ailuropoda1864

import subprocess
import os
import time


# put your GA directory here, e.g. '/home/fay/code/GA/DSI'
CWD = ''

# put the path to a .gitignore template here
# e.g. '/home/fay/code/GA/DSI/projects/West-Nile-Virus-Prediction/.gitignore'

Ailuropoda1864

import numpy as np
import pandas as pd
from pandas.core.dtypes.common import (
    is_numeric_dtype, is_datetime64_dtype, is_bool_dtype
)
from pandas.core.indexes.datetimes import DatetimeIndex


def eda(dataframe, head=True, info=True, describe=True, duplicated=True,
        dup_kwd={}):

Ailuropoda1864

import pandas as pd
from pandas.core.dtypes.common import (
    is_numeric_dtype, is_datetime64_dtype, is_bool_dtype
)


def category_counts(dataframe, max_nunique=20, numeric=False, datetime=False):
    """
    prints value counts for each (categorical) column
    :param dataframe: a pandas DataFrame

Ailuropoda1864

import pandas as pd

def find_duplicated(dataframe, show=True, sort=False):
    """
    prints out information on duplicate rows
    :param dataframe: a pandas DataFrame
    :param show: boolean; if True, the duplicated rows (if any) are shown
    :param sort: boolean; if True, the duplicated rows are sorted by each column
                 of the dataframe
    """

Ailuropoda1864

import pandas as pd

def show_null(dataframe):
    """
    prints the number and percentage of null values in each column 
    :param dataframe: a pandas DataFrame
    :return: None
    """
    if dataframe.isnull().sum().sum() == 0:
        print('No null in the dataframe.')

Ailuropoda1864

import numpy as np
import pandas as pd
from pandas.core.dtypes.common import (
    is_numeric_dtype, is_datetime64_dtype, is_bool_dtype
)
from pandas.core.indexes.datetimes import DatetimeIndex


def describe_by_type(dataframe):
    """