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process_data.py
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process_data.py
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# import libraries
import sys
import pandas as pd
from sqlalchemy import create_engine
def load_data(messages_filepath, categories_filepath):
"""
Load messages and categories from csv files into a pandas DF.
INPUT:
messages_filepath - path to location of messages csv file
categories_filepath - path to location of categories csv files
OUTPUT:
df - pandas DF with messages and categories
"""
# load messages dataset
messages = pd.read_csv(messages_filepath)
# load categories dataset
categories = pd.read_csv(categories_filepath)
# merge datasets
df = pd.merge(messages, categories, on='id')
return df
def clean_data(df):
"""
Clean data in a pandas DF.
Clean data in a pandas DF by renaming category columns, convert
category values to 0 or 1 and drop duplicates.
INPUT:
df - pandas dataframe with messages and categories in source format
OUTPUT:
df - cleaned pandas dataframe with messages and categories
"""
# create a dataframe of the 36 individual category columns
categories = df['categories'].str.split(';', expand=True)
# select the first row of the categories dataframe
row = categories.iloc[0]
# use this row to extract a list of new column names for categories.
# one way is to apply a lambda function that takes everything
# up to the second to last character of each string with slicing
category_colnames = list(map(lambda col: col[:-2], row))
# rename the columns of `categories`
categories.columns = category_colnames
for column in categories:
# set each value to be the last character of the string
categories[column] = categories[column].str[-1:]
# convert column from string to numeric
categories[column] = categories[column].astype(int)
# drop the original categories column from `df`
df = df.drop(['categories'], axis=1)
# concatenate the original dataframe with the new `categories` dataframe
df = pd.concat([df, categories], axis=1, sort=False)
# drop duplicates
df = df.drop_duplicates()
return df
def save_data(df, database_filename):
"""
Save data from a pandas df into SQLite database.
INPUT:
df -- pandas DF with messages and categories
database_filename -- path to location of database file
OUTPUT:
"""
engine = create_engine('sqlite:///{}'.format(database_filename))
df.to_sql('DisasterResponse', engine, index=False)
def main():
if len(sys.argv) == 4:
messages_filepath, categories_filepath, database_filepath = sys.argv[1:]
print('Loading data...\n MESSAGES: {}\n CATEGORIES: {}'
.format(messages_filepath, categories_filepath))
df = load_data(messages_filepath, categories_filepath)
print('Cleaning data...')
df = clean_data(df)
print('Saving data...\n DATABASE: {}'.format(database_filepath))
save_data(df, database_filepath)
print('Cleaned data saved to database!')
else:
print('Please provide the filepaths of the messages and categories ' \
'datasets as the first and second argument respectively, as ' \
'well as the filepath of the database to save the cleaned data ' \
'to as the third argument. \n\nExample: python process_data.py ' \
'disaster_messages.csv disaster_categories.csv ' \
'DisasterResponse.db')
if __name__ == '__main__':
# to debug
if len(sys.argv) == 0:
sys.argv = ['.', './data/disaster_messages.csv', './data/disaster_categories.csv', './data/DisasterResponse.db']
# run main thread
main()