final1.csv

#capture prevents the output of the wget from showing
%%capture
!wget "http://www.csc.calpoly.edu/~dekhtyar/466-Fall2019/data/babynames/NationalNames.csv"
!wget "http://www.csc.calpoly.edu/~dekhtyar/466-Fall2019/data/babynames/StateNames.csv"
!wget "https://raw.githubusercontent.com/maciverlab/ME224_public/main/homework/data_forensics/found_on_phone_cleaned.csv"
!wget  "https://raw.githubusercontent.com/Aokehua23132/Testing/main/

reading files
def read_csv(filename):
  csv=[]
  with open(filename, "r") as file:
    lines = file.readlines()
    headers = lines[0].replace("\n","").split(",")
    for line in lines[1:]:
      values = line.replace("\n","").split(",")
      csv.append ({headers[i]: values[i] for i in range(len(values))} )
  return csv
csv = read_csv('/content/NationalNames.csv')


pandas (not specific for numbers)
# We start with importing the package
import pandas as pd

# load the national baby names database
df = pd.read_csv("NationalNames.csv")
# show first 5 rows
df.head(5)


# load the state baby names database
state_df = pd.read_csv("StateNames.csv")
# Show the last 5 rows
state_df.tail(5)


# The columns are the labels across the top
print(state_df.columns[1])
print(type(state_df.columns[1]))
# The indexes run down the side (rows)
print(state_df.index)

# Show the basic info of your data: how many rows and columns, and what are they?
df.info()

#use sort_values to sort by year descending
sorted_df = df.sort_values(by="Name", ascending=True)
sorted_df.head(5)

or 

#use sort_values to sort by year and count descending
two_key_sorted_df= df.sort_values(by=["Year","Count"], ascending=False)
two_key_sorted_df.head(5)

# We can access a column with the . notation shown here

df.Count (gives us the entire index with counts and length)
or 
# Or multiple columns using a list to index
# note: make sure you use double square brackets [["column_1","column_2"]]
df[["Name","Count"]]

# We can slice like in a list. Remember, dataframe is like a 2D list,
df[10:11]

# so the code below will get all the columns
# Notice how the row indexes are numeric and that's what we slice on

# The operation order doesn't alter the result
# Rever the column selection and slicing operation, check the results
df[:10][["Year", "Name"]]

#filter all Male names from this century in the state of Illinois
state_df[state_df.Gender == "M"][state_df.Year >= 2000][state_df.State == "IL"]

# print loc[0]
print(sorted_df.loc[0])
# print iloc[0]
print()
print(sorted_df.iloc[0])
loc is strictly labeled based, iloc is integer based and works on indexes

df.info() gives us info on the data frames

path = df[['latitude', 'longitude']]
kms = df['kms']

vel = df['gps_currKPH'] # velocity in kph
vel_m_per_s = vel*(10/36)

import matplotlib.pyplot as plt
plt.plot(df['currBPM'],color='green')
plt.plot(df['gps_currKPH'],color='blue')


import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv("Raw Data.csv")

df.columns
# how do we see the difference in time between samples?
# hint: do you recall a matlab function that does this?

rawt = df['Time (s)']
rawx = df['Linear Acceleration x (m/s^2)']
rawy = df['Linear Acceleration y (m/s^2)']
rawz = df['Linear Acceleration z (m/s^2)']

plt.subplot(1,4,1)
plt.plot(rawt,rawx, 'r-')

plt.subplot(1,4,2)
plt.plot(rawt,rawy, 'g-')

plt.subplot(1,4,3)
plt.plot(rawt,rawz, 'b-')

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv("Raw Data.csv")


rawt = df['Time (s)']
raw_out = df['Linear Acceleration y (m/s^2)'] # out axis is y
raw_up = df['Linear Acceleration z (m/s^2)'] #
raw_right = df['Linear Acceleration x (m/s^2)']

plt.figure(figsize=(12,5))

plt.subplot(1,4,1)
plt.plot(rawt, raw_out, 'r-')

plt.subplot(1,4,2)
plt.plot(rawt, raw_up, 'b-')

plt.subplot(1,4,3)
plt.plot(rawt, raw_right, 'g-')


--------------------
# Pandas, and install the folium package, which lets you work with
# maps, and numpy
%%capture
!pip install folium

#use sort_values to sort by year descending
sorted_df = df.sort_values(by="Name", ascending=True)
sorted_df.head(5)

import folium

token = "pk.eyJ1IjoiZ2VybWFuZXNwaW5vc2EiLCJhIjoiY2x0NHVmcG1hMDZucjJpcXE1MzQ0Ym55eCJ9.byXqX6eUYcwDbOEZ1d05-g"

tileurl = 'https://api.mapbox.com/v4/mapbox.satellite/{z}/{x}/{y}@2x.png?access_token=' + str(token)

path = df[['latitude', 'longitude']]

map = folium.Map(location=[path.latitude.mean(), path.longitude.mean()],
                 zoom_start = 10,
                 tiles=tileurl,
                 control_scale=True,
                 attr='Mapbox')
path_taken = folium.PolyLine(path).add_to(map)
folium.Circle(path[0:1], color="green").add_to(map)
folium.Circle(path[-1:], color="red").add_to(map)

map
----------
import folium

# Coordinates of New York City
latitude = 40.7128
longitude = -74.0060

# Create a map centered around New York City
map_nyc = folium.Map(location=[latitude, longitude], zoom_start=10)

# Add a marker for New York City
folium.Marker([latitude, longitude], popup='New York City').add_to(map_nyc)

# Display the map
map_nyc

-------------------
import folium

# Create a map centered around the USA
map_usa = folium.Map(location=[37.0902, -95.7129], zoom_start=4)

# Cities coordinates
cities = {
    "New York City": (40.7128, -74.0060),
    "Los Angeles": (34.0522, -118.2437),
    "Chicago": (41.8781, -87.6298)
}

# Add markers for each city
for city, coords in cities.items():
    folium.Marker(location=coords, 
                  popup=city, 
                  icon=folium.Icon(icon='cloud')).add_to(map_usa)

# Display the map
map_usa

------------
import folium
from folium.plugins import HeatMap

# Sample data: locations in San Francisco (latitude, longitude)
locations = [
    (37.7749, -122.4194), # San Francisco
    (37.7786, -122.4892), # Golden Gate Park
    (37.7975, -122.4143), # Fisherman's Wharf
]

# Create a map centered around San Francisco
map_sf = folium.Map(location=[37.7749, -122.4194], zoom_start=13)

# Create a HeatMap
HeatMap(locations).add_to(map_sf)

# Display the map
map_sf