update PIC16B#3

_posts/2021-04-13-HW-1.md

@@ -146,7 +146,7 @@ Now, import and visualize the countries dataset:
 # this is the countries data
 countries_url = "https://raw.githubusercontent.com/mysociety/gaze/master/data/fips-10-4-to-iso-country-codes.csv"
 countries = pd.read_csv(countries_url)
-# countries.head()
+countries.head()
 ```
 <div>
 <style scoped>
@@ -212,7 +212,7 @@ Now, import and visualize the stations dataset:
 # this is the stations data
 stations_url = "https://raw.githubusercontent.com/PhilChodrow/PIC16B/master/datasets/noaa-ghcn/station-metadata.csv"
 stations = pd.read_csv(stations_url)
-# stations.head()
+stations.head()
 ```
 <div>
 <style scoped>
@@ -600,7 +600,7 @@ query_climate_database(country = "India",
       <td>...</td>
     </tr>
     <tr>
-      <th>7965</th>
+      <th>3147</th>
       <td>DARJEELING</td>
       <td>27.050</td>
       <td>88.270</td>
@@ -610,7 +610,7 @@ query_climate_database(country = "India",
       <td>5.10</td>
     </tr>
     <tr>
-      <th>7966</th>
+      <th>3148</th>
       <td>DARJEELING</td>
       <td>27.050</td>
       <td>88.270</td>
@@ -620,7 +620,7 @@ query_climate_database(country = "India",
       <td>6.90</td>
     </tr>
     <tr>
-      <th>7967</th>
+      <th>3149</th>
       <td>DARJEELING</td>
       <td>27.050</td>
       <td>88.270</td>
@@ -630,7 +630,7 @@ query_climate_database(country = "India",
       <td>8.10</td>
     </tr>
     <tr>
-      <th>7968</th>
+      <th>3150</th>
       <td>DARJEELING</td>
       <td>27.050</td>
       <td>88.270</td>
@@ -640,7 +640,7 @@ query_climate_database(country = "India",
       <td>5.60</td>
     </tr>
     <tr>
-      <th>7969</th>
+      <th>3151</th>
       <td>DARJEELING</td>
       <td>27.050</td>
       <td>88.270</td>
@@ -651,10 +651,11 @@ query_climate_database(country = "India",
     </tr>
   </tbody>
 </table>
-<p>7970 rows × 7 columns</p>
+<p>3152 rows × 7 columns</p>
 </div>
 
-Now, get an almost similar dataset as the example one. However, its dimension is (7970, 7) instead of (3152, 7). I'm really confused right here. Any comments and suggestions would helps a lot. Thank you! :)
+Nice! Now get the exact database as desired.
+- Small remark: when want to rewrite the whole database, simply delete it from the files.
 
 ## §3. Geographical Scatter Function
 
@@ -664,8 +665,8 @@ Now, get an almost similar dataset as the example one. However, its dimension is
 	- `country`, a string giving the name of a country for which data should be returned.
 	- `year_begin` and `year_end`, two integers giving the earliest and latest years for which should be returned.
 	- `month`, an integer giving the month of the year for which should be returned.
-	- `min_obs`, the minimum required number of years of data for any given station. Only data for stations with at least min_obs years worth of data in the specified month should be plotted; the others should be filtered out. df.transform() plus filtering is a good way to achieve this task.
-	- `**kwargs`, additional keyword arguments passed to px.scatter_mapbox(). These can be used to control the colormap used, the mapbox style, etc.
+	- `min_obs`, the minimum required number of years of data for any given station. Only data for stations with at least `min_obs` years worth of data in the specified month should be plotted; the others should be filtered out. `df.transform()` plus filtering is a good way to achieve this task.
+	- `**kwargs`, additional keyword arguments passed to `px.scatter_mapbox()`. These can be used to control the colormap used, the mapbox style, etc.
 
 - Output Contents:
 	- an interactive geographic scatterplot constructed using Plotly Express as described in `INTRO` part.
@@ -694,11 +695,57 @@ def coef(data_group):
 Now, start writing the plotting-return function:
 
 ```python
-
+def temperature_coefficient_plot(country, year_begin, year_end, month, min_obs, **kwargs):
+
+    """
+    plot heat-colored map of stations in a specific country
+    and specified month, with specified time interval
+    
+    Input
+    --------
+    country: string, tell which country for database to return
+    year_begin: int, earliest year to be returned
+    year_end: int, last year to be returned
+    month: int, month of the year to be returned
+    min_obs: the minimum years of observations
+    **kwargs: random keyword arguments to be applied in the plotting
+    
+    Output
+    --------
+    The temperature plot 
+    """
+
+    # 1. write desired dataset from database with query function
+    df = query_climate_database(country, year_begin, year_end, month)
+
+    # 2. only keep stations with years more than min_obs
+    # store the number of years considered as year_num variable for each row entry
+    num_years = data.groupby(["NAME"])["Year"].transform("count")
+    # cut the dataset with those stations only greater than min_obs
+    df = df[num_years >= min_obs]
+
+    # 3. get yearly temperature average increase dataset
+    yearly_temp_increase = df.groupby(["NAME", "Month"]).apply(coef).reset_index()
+    # rename column as yearly_temperature_increase
+    yearly_temp_increase = yearly_temp_increase.rename(columns={0: "Yearly Temperature Increase"})
+
+    # 4. append geographic spotpoints (LATI & LONG)
+    # create temporary dataset with only geographic INFO
+    spots = df[["NAME", "LATITUDE", "LONGITUDE"]].drop_duplicates()
+    # append spotpoints locations by merging two datasets
+    final = pd.merge(yearly_temp_increase, spots, on = ["NAME"])
+
+    # 5. create the graph
+    fig = px.scatter_mapbox(final, hover_name = "NAME",
+                            lat = "LATITUDE", lon = "LONGITUDE",
+                            color = "Yearly Temperature Increase", **kwargs)
+    return fig
 ```
 
 ### Step 2: Sample Output Check
 
+For example, create a plot of estimated yearly increases in temperature during the month of January, in the interval 1980-2020, in India.
+
 ```python
 color_map = px.colors.diverging.RdGy_r # choose a colormap
 fig = temperature_coefficient_plot("India", 1980, 2020, 1,