import re
import csv
import pandas as pd

# DOJ State Crime Estimates
csv_file = 'CrimeStatebyState.csv'

# Variable for keeping track of current state in the file
state = ''
# Store the output (list of dictionaries, for importing into pandas)
data = []
with open(csv_file) as f:
    # Use csv module to read file
    reader = csv.reader(f)
    for line in reader:
        # Skip blank lines
        if len(line) > 0:
            if line[0][:9] == 'Estimated':
                #regular expression
                matches = re.search('Estimated crime in (.*)$', line[0])
                state = matches.group(1)
            elif state and state != 'United States-Total' and line[0] != 'Year':
                state_dict = {
                    'State': state, 
                    'Year': line[0], 
                    'Population': line[1], 
                    'Violent crime total': line[2]
                }
                data.append(state_dict)

df = pd.DataFrame(data)
print(df)

        State  Year Population Violent crime total
0     Alabama  1985    4021000               18398
1     Alabama  1986    4053000               22616
2     Alabama  1987    4083000               22833
3     Alabama  1988    4127000               23052
4     Alabama  1989    4118000               24329
...       ...   ...        ...                 ...
1525  Wyoming  2010     564554                1117
1526  Wyoming  2011     567356                1245
1527  Wyoming  2012     576626                1161
1528  Wyoming  2013     583223                1212
1529  Wyoming  2014     584153                1142

[1530 rows x 4 columns]

# Part B
import re
import csv
import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt

#read txt file
with open("C:/Users/ashle/Downloads/lab11/worldcities.txt", "r", encoding="utf-8") as f:
    lines = f.readlines() 

# DMS lat & long
pattern = re.compile(
    r"^(.*?)\t"                 # City/Place name
    r"(\d+)°(\d+)'([NS])\t"     # Latitude DMS
    r"(\d+)°(\d+)'([EW])"       # Longitude DMS
)
def dms_to_dd(deg, minutes, hemi):
    dec = float(deg) + float(minutes)/60
    if hemi in ["S", "W"]:
        dec *= -1
    return dec


records = []
for line in lines:
    m = pattern.search(line)
    if m:
        city = m.group(1).strip()
        lat_deg, lat_min, lat_dir = m.group(2), m.group(3), m.group(4)
        lon_deg, lon_min, lon_dir = m.group(5), m.group(6), m.group(7)
        lat_dd = dms_to_dd(lat_deg, lat_min, lat_dir)
        lon_dd = dms_to_dd(lon_deg, lon_min, lon_dir)

        records.append([city, lat_dd, lon_dd])
#dataframe
df = pd.DataFrame(records, columns = ['city', 'latitude', 'longitude'])
#geodataframe
gdf = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df["longitude"], df["latitude"]),
    crs="EPSG:4326")
#map with geodataframe
# Download and use the data directly
import requests
import io
url = "https://naciscdn.org/naturalearth/110m/cultural/ne_110m_admin_0_countries.zip"
r = requests.get(url)
world = gpd.read_file(io.BytesIO(r.content))

ax = world.plot(figsize=(15, 10), edgecolor="black", color="white")
gdf.plot(ax=ax, color="red", markersize=40)

ax.set_title("World Cities Converted from DMS to Decimal Degrees")

C:\Users\ashle\anaconda3\Lib\site-packages\pyogrio\core.py:35: RuntimeWarning: Could not detect GDAL data files.  Set GDAL_DATA environment variable to the correct path.
  _init_gdal_data()

Text(0.5, 1.0, 'World Cities Converted from DMS to Decimal Degrees')