satishkt

def clean_data(filenames):
dfs=[]
for filename in filenames:
epic_df = pd.read_csv(filename,names=['col'],header=None)
epic_df['cuisine']=epic_df['col'].apply(lambda x : x.split('\t')[0])
epic_df['ingredients'] = epic_df['col'].apply(lambda x:(',').join (x.split('\t')[1:]))
epic_df.drop('col',inplace=True,axis=1)
dfs.append(epic_df)
return dfs

satishkt

                        cuisine  ingredients
    cuisine_group                           
    African            0.637300     0.637300
    EastAsian          3.231764     3.231764
    EasternEuropean    0.689805     0.689805
    LatinAmerican      5.255916     5.255916
    MiddleEastern      1.167780     1.167780
    NorthAmerican     75.179693    75.179693
    NorthernEuropean   0.452628     0.452628
    SouthAsian         1.124328     1.124328

satishkt

"""
Reads Google search history JSON files from the given directory.
Expected JSON format :
{"event":[
 {"query":{"id":[{"timestamp_usec":"1135905619017279"}],"query_text":"XYZ"}},
 {"query":{"id":[{"timestamp_usec":"1135903586447380"}],"query_text":"ABC"}},
]}


The folder containing the JSON files is stored in a config.ini file with the section

satishkt

from wordcloud import WordCloud
wordcloud = WordCloud().generate(text)
# Display the generated image:
# the matplotlib way:
import matplotlib.pyplot as plt
plt.imshow(wordcloud)
plt.axis("off")
# take relative word frequencies into account, lower max_font_size
wordcloud = WordCloud(max_font_size=40, relative_scaling=.5).generate(text)
plt.figure()

satishkt

__author__='satish'
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly as ply
from sklearn.feature_extraction.text import CountVectorizer,HashingVectorizer
from sklearn.cross_validation import train_test_split
from sklearn.naive_bayes import BernoulliNB
from sklearn.naive_bayes import MultinomialNB,GaussianNB
from sklearn.linear_model import LogisticRegression,RidgeClassifier,Perceptron,PassiveAggressiveClassifier

satishkt

#Code snippets for Pandas
import pandas as pd
‘’’
Reading Files, Selecting Columns, and Summarizing
‘’’
# reading in a file from local computer or directly from a URL
# various file formats that can be read in out wrote out
‘’’
Format Type     Data Description      Reader           Writer
text                  CSV            read_csv          to_csv

satishkt

def namedtuple_to_str(t, field_widths=15):
    if isinstance(field_widths, int):
        field_widths = [field_widths] * len(t._fields)
    field_pairs = ['{}={}'.format(field, getattr(t, field)) for field in t._fields]
    s = ' '.join('{{:{}}}'.format(w).format(f) for w,f in zip(field_widths, field_pairs))
    result = '{}( {} )'.format(type(t).__name__, s)
    return result

satishkt

brew update
brew tap adoptopenjdk/openjdk
brew search jdk
brew cask install adoptopenjdk11
brew cask install adoptopenjdk12
brew cask install adoptopenjdk13
# Check where Java is installed
/usr/libexec/java_home -V
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