pietrocolombo

def make_mean_variance(dataframe):
    new_row = {}
    
    for field in ['user', 'gender', 'age', 'how_tall_in_meters', 'weight', 'body_mass_index', 'class']:
        new_row[field] = dataframe[field].iloc[0]
    
    for field in ['roll1', 'pitch1', 'roll2', 'pitch2', 'roll3', 'pitch3','roll4', 'pitch4']:
        new_row[field] = np.var(dataframe[field])
        
    for i in range (1, 5):

pietrocolombo

import pandas as pd

contacts = pd.read_csv("contacts.csv")

contacts.drop_duplicates(subset=['Numero di telefono']) # name of the column that identifies the telephone number

# clears numbers that have more than 13 digits
index_to_drop = contacts[contacts['Numero di telefono'].map(len) != 13].index
contacts.drop(index_to_drop, inplace=True)

pietrocolombo

#include <stdio.h>
#include <stdlib.h>

typedef struct listas {
    int info;
    struct listas * next;
} list_t;

int listlen (list_t *);
list_t * listaddhead (list_t *, int);

pietrocolombo

# let's add information to our dataset
start_time <- Sys.time()
# function to transform angles deg into radians
deg2rad <- function(deg) {(deg * pi) / (180)}

if(!require(geosphere)){
  install.packages("geosphere")
  library("geosphere")
}

pietrocolombo

# let's add information to our dataset
start_time <- Sys.time()
# function to transform angles deg into radians
deg2rad <- function(deg) {(deg * pi) / (180)}

if(!require(geosphere)){
  install.packages("geosphere")
  library("geosphere")
}

pietrocolombo

# delete_duplicate_file.py
# Python 3.8.6

"""
Given a folder, walk through all files within the folder and subfolders
and delete all file that are duplicates so you have only one copy of every file
The md5 checcksum for each file will determine the duplicates
"""

import os

pietrocolombo

import logging
import logging.handlers

# Enable logging
logging.basicConfig(
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    level=logging.INFO, filename='log.log')

logger = logging.getLogger(__name__)

pietrocolombo

if(!require(corrplot)){
  install.packages("corrplot")
  library("corrplot")
}
if(!require(RGraphics)){
  install.packages("RGraphics")
  library("RGraphics")
}
if(!require(grid)){
  install.packages("grid")

pietrocolombo

# query to OpenStreetMap or bing to obtain information on the state, city, 
# region both on the starting and ending point of each route

if(!require(revgeo)){
  install.packages("revgeo")
  library("revgeo")
}

# I read the csv generated by the script compress_database.r

pietrocolombo

# from the n lines that identify me a path I get one line per path

# I load the csv that the add_feature_consistency.r script generated for me
perc_csv <- "dataset_with_add_features.csv"
dati <- read.csv(perc_csv, header = TRUE, sep =",", quote = "\"", dec = ".")

# we remove the file that generates this script
file.remove("dataset_compresso.csv")