MLWhiz

class SimpleDialogueManager(object):
    """
    This is a simple dialogue manager to test the telegram bot.
    The main part of our bot will be written here.
    """
    def __init__(self):
        # Instantiate all the models and TFIDF Objects.
        print("Loading resources...")
        # Instantiate a Chatterbot for Chitchat type questions
        from chatterbot import ChatBot

MLWhiz

# We dont Probably need the Gridlines. Do we? If yes comment this line
sns.set(style="ticks")

# Here we create a matplotlib axes object. The extra parameters we use
# "ci" to remove confidence interval
# "marker" to have a x as marker.
# "scatter_kws" to provide style info for the points.[s for size]
# "line_kws" to provide style info for the line.[lw for line width]

g = sns.regplot(x="tip", y="total_bill", data=tips, ci = False,

MLWhiz

# So this function creates a faceted plot. The plot is parameterized by the following:

# col : divides the data points into days and creates that many plots
# palette: deep, muted, pastel, bright, dark, and colorblind. change the colors in graph. Experiment with these
# col_wrap: we want 2 graphs in a row? Yes.We do
# scatter_kws: attributes for points
# hue: Colors on a particular column.
# size: controls the size of graph

g = sns.lmplot(x="tip", y="total_bill",ci=None,data=tips, col="day",

MLWhiz

sns.set(style="ticks")

flatui = ["#9b59b6", "#3498db", "#95a5a6", "#e74c3c", "#34495e", "#2ecc71"]

# This Function takes as input a custom palette
g = sns.barplot(x="sex", y="tip", hue="day",
	palette=sns.color_palette(flatui),data=tips,ci=None)

# remove the top and right line in graph
sns.despine()

MLWhiz

# Create a list of 1000 Normal RVs
x = np.random.normal(size=1000)

sns.set_context("poster")
sns.set_style("ticks")
# This  Function creates a normed Histogram by default.
# If we use the parameter kde=False and norm_hist=False then
# we will be using a count histogram

g=sns.distplot(x,

MLWhiz

import scipy.stats as stats

a = 1.5
b = 1.5
x = np.arange(0.01, 1, 0.01)
y = stats.beta.rvs(a,b,size=10000)
y_act = stats.beta.pdf(x,a,b)
g=sns.distplot(y,kde=False,norm_hist=True,
            kde_kws={"color":"g","lw":4,"label":"KDE Estim","alpha":0.5},
            hist_kws={"color":"r","alpha":0.3,"label":"Freq"})

MLWhiz

# Create a Pairplot
g = sns.pairplot(iris,hue="species",palette="muted",size=5,
	vars=["sepal_width", "sepal_length"],kind='reg',markers=['o','x','+'])

# To change the size of the scatterpoints in graph
g = g.map_offdiag(plt.scatter,  s=35,alpha=0.5)

# remove the top and right line in graph
sns.despine()
# Additional line to adjust some appearance issue

MLWhiz

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
# We dont Probably need the Gridlines. Do we? If yes comment this line
sns.set(style="ticks")
player_df = pd.read_csv("../input/data.csv")
numcols = [
 'Overall',

MLWhiz

def wage_split(x):
    try:
        return int(x.split("K")[0][1:])
    except:
        return 0
player_df['Wage'] = player_df['Wage'].apply(lambda x : wage_split(x))
def value_split(x):
    try:
        if 'M' in x:
            return float(x.split("M")[0][1:])

MLWhiz

corr = player_df.corr()
g = sns.heatmap(corr,  vmax=.3, center=0,
            square=True, linewidths=.5, cbar_kws={"shrink": .5}, annot=True, fmt='.2f', cmap='coolwarm')
sns.despine()
g.figure.set_size_inches(14,10)
    
plt.show()