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# Drop the columns where all elements are missing values:
df.dropna(axis=1, how='all')

# Drop the columns where any of the elements are missing values
df.dropna(axis=1, how='any')

# Keep only the rows which contain 2 missing values maximum
df.dropna(thresh=2)

# Drop the columns where any of the elements are missing values

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import numpy as np
import pandas as pd

from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix

from keras.models import Sequential
from keras.layers import Dense

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## Prepare Scatter Plot with Best Fit Line

#Set Working Directory using setwd("")

#Read Data
data=read.csv("SVM.csv", header=T)
head(data)

#Scatter Plot
plot(data, main ="Scatter Plot")

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# Hashtags & mentions
tag_dict = {}
mention_dict = {}

for i in tweet_df.index:
    tweet_text = tweet_df.ix[i]['Tweet text']
    tweet = tweet_text.lower()
    tweet_tokenized = tweet.split()

    for word in tweet_tokenized:

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

def descriptive_stats(distribution):

	'''
	Compute and present simple descriptive stats for a distribution
	
	Parameters

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#Install the Ecdat package
install.packages("Ecdat")
#Loading the library and the Garch dataset
library(Ecdat)
mydata=Garch
#Look at the dataset
str(mydata)

#Correct the data types of date and day
#Correcting date fixes it to some arbitrary date such that the trend is same but the mapping is different

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from keras import models
from keras.layers import Dense, Dropout
from keras.utils import to_categorical
from keras.datasets import mnist
from keras.utils.vis_utils import model_to_dot
from IPython.display import SVG

import livelossplot
plot_losses = livelossplot.PlotLossesKeras()

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import numpy

import GA



"""

The y=target is to maximize this equation ASAP:

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from collections import Counter
from string import punctuation
from sklearn.feature_extraction.stop_words import ENGLISH_STOP_WORDS as stop_words
import spacy

def count_words(tokens):
    word_counts = {}
    for token in tokens:
        if token not in stop_words and token not in punctuation and token is not '\n':
            if token not in word_counts.keys():

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"""
Creates a corpus from Wikipedia dump file.

Inspired by:
https://github.com/panyang/Wikipedia_Word2vec/blob/master/v1/process_wiki.py
"""

import sys
from gensim.corpora import WikiCorpus