update solutions for JupyterLite

content/notebooks/5. More Pandas.ipynb

@@ -1078,9 +1078,7 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "df['city'].value_counts()"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",

content/solutions/04_06.py

@@ -9,4 +9,9 @@ def f(x):
     """Returns the argument multiplied by 3 and increased by 10."""
     return (x * 3) + 10
 
-print(f(4)) # x = 2 is arbitrary. Try with other values.
+
+print('def f(x):')
+print('\t"""Returns the argument multiplied by 3 and increased by 10."""')
+print('\treturn (x * 3) + 10\n')
+
+print('f(x) = ', f(4)) # x = 4 is arbitrary. Try with other values.

content/solutions/05_27.py

@@ -1,3 +1,26 @@
+import pandas as pd
+
+df_2014 = pd.read_csv("../data/food_training/training_2014.csv", header=1)
+df_2015 = pd.read_csv("../data/food_training/training_2015.csv", header=1)
+df_2016 = pd.read_csv("../data/food_training/training_2016.csv", header=1)
+
+frames = [df_2014, df_2015, df_2016]
+df = pd.concat(frames)
+
+df = df.reset_index()
+df.index
+
+cols_to_remove = ["Unnamed: 5", "Unnamed: 6"]
+df = df.drop(cols_to_remove, axis=1)
+
+df[["city", "country"]] = df["Location"].str.split(pat=";", expand=True)
+
+df = df.drop("Location", axis=1)
+
+df["city"] = df["city"].str.lower()
+
+df["city"] = df["city"].str.replace(r"/\w*", "", regex=True)
+
 dict_codes = {
     "BG": "Bulgaria",
     "CZ": "Czech Republic",
@@ -8,4 +31,6 @@
 }
 
 country_in_codes = df["country"].isin(dict_codes.keys())
-df.loc[country_in_codes, "country"] = df.loc[country_in_codes, "country"].map(dict_codes)
+df.loc[country_in_codes, "country"] = df.loc[country_in_codes, "country"].map(dict_codes)
+
+print('df.loc[country_in_codes, "country"] = df.loc[country_in_codes, "country"].map(dict_codes)')

content/solutions/05_28.py

@@ -1 +1,38 @@
-df.loc[df["city"] == "unknown", "country"]
+import pandas as pd
+
+df_2014 = pd.read_csv("../data/food_training/training_2014.csv", header=1)
+df_2015 = pd.read_csv("../data/food_training/training_2015.csv", header=1)
+df_2016 = pd.read_csv("../data/food_training/training_2016.csv", header=1)
+
+frames = [df_2014, df_2015, df_2016]
+df = pd.concat(frames)
+
+df = df.reset_index()
+df.index
+
+cols_to_remove = ["Unnamed: 5", "Unnamed: 6"]
+df = df.drop(cols_to_remove, axis=1)
+
+df[["city", "country"]] = df["Location"].str.split(pat=";", expand=True)
+
+df = df.drop("Location", axis=1)
+
+df["city"] = df["city"].str.lower()
+
+df["city"] = df["city"].str.replace(r"/\w*", "", regex=True)
+
+dict_codes = {
+    "BG": "Bulgaria",
+    "CZ": "Czech Republic",
+    "IT": "Italy",
+    "GR": "Greece",
+    "SI": "Slovenia",
+    "UK": "United Kingdom",
+}
+
+country_in_codes = df["country"].isin(dict_codes.keys())
+df.loc[country_in_codes, "country"] = df.loc[country_in_codes, "country"].map(dict_codes)
+
+print('df.loc[df["city"] == "unknown", "country"]\n')
+
+display(df.loc[df["city"] == "unknown", "country"])

content/solutions/05_29.py

@@ -1,3 +1,26 @@
+import pandas as pd
+
+df_2014 = pd.read_csv("../data/food_training/training_2014.csv", header=1)
+df_2015 = pd.read_csv("../data/food_training/training_2015.csv", header=1)
+df_2016 = pd.read_csv("../data/food_training/training_2016.csv", header=1)
+
+frames = [df_2014, df_2015, df_2016]
+df = pd.concat(frames)
+
+df = df.reset_index()
+df.index
+
+cols_to_remove = ["Unnamed: 5", "Unnamed: 6"]
+df = df.drop(cols_to_remove, axis=1)
+
+df[["city", "country"]] = df["Location"].str.split(pat=";", expand=True)
+
+df = df.drop("Location", axis=1)
+
+df["city"] = df["city"].str.lower()
+
+df["city"] = df["city"].str.replace(r"/\w*", "", regex=True)
+
 dict_capitals = {
     "Denmark": "copenhague",
     "France": "paris",
@@ -7,4 +30,7 @@
 }
 
 unknown_city = df["city"] == "unknown"
-df.loc[unknown_city, "city"] = df.loc[unknown_city, "country"].map(dict_capitals)
+df.loc[unknown_city, "city"] = df.loc[unknown_city, "country"].map(dict_capitals)
+
+print('unknown_city = df["city"] == "unknown"')
+print('df.loc[unknown_city, "city"] = df.loc[unknown_city, "country"].map(dict_capitals)')

content/solutions/05_30.py

@@ -1 +1,39 @@
-set(df["city"]) - dict_cities.keys()
+import pandas as pd
+
+df_2014 = pd.read_csv("../data/food_training/training_2014.csv", header=1)
+df_2015 = pd.read_csv("../data/food_training/training_2015.csv", header=1)
+df_2016 = pd.read_csv("../data/food_training/training_2016.csv", header=1)
+
+frames = [df_2014, df_2015, df_2016]
+df = pd.concat(frames)
+
+df = df.reset_index()
+df.index
+
+cols_to_remove = ["Unnamed: 5", "Unnamed: 6"]
+df = df.drop(cols_to_remove, axis=1)
+
+df[["city", "country"]] = df["Location"].str.split(pat=";", expand=True)
+
+df = df.drop("Location", axis=1)
+
+df["city"] = df["city"].str.lower()
+
+df["city"] = df["city"].str.replace(r"/\w*", "", regex=True)
+
+dict_capitals = {
+    "Denmark": "copenhague",
+    "France": "paris",
+    "Italy": "rome",
+    "Spain": "madrid",
+    "United Kingdom": "london",
+}
+
+unknown_city = df["city"] == "unknown"
+df.loc[unknown_city, "city"] = df.loc[unknown_city, "country"].map(dict_capitals)
+
+dict_cities = df.loc[df['country'].notnull(), ['city', 'country']].set_index('city').to_dict()['country']
+
+print('set(df["city"]) - dict_cities.keys()\n')
+
+display(set(df["city"]) - dict_cities.keys())

content/solutions/05_31.py

@@ -1,3 +1,39 @@
+import pandas as pd
+
+df_2014 = pd.read_csv("../data/food_training/training_2014.csv", header=1)
+df_2015 = pd.read_csv("../data/food_training/training_2015.csv", header=1)
+df_2016 = pd.read_csv("../data/food_training/training_2016.csv", header=1)
+
+frames = [df_2014, df_2015, df_2016]
+df = pd.concat(frames)
+
+df = df.reset_index()
+df.index
+
+cols_to_remove = ["Unnamed: 5", "Unnamed: 6"]
+df = df.drop(cols_to_remove, axis=1)
+
+df[["city", "country"]] = df["Location"].str.split(pat=";", expand=True)
+
+df = df.drop("Location", axis=1)
+
+df["city"] = df["city"].str.lower()
+
+df["city"] = df["city"].str.replace(r"/\w*", "", regex=True)
+
+dict_capitals = {
+    "Denmark": "copenhague",
+    "France": "paris",
+    "Italy": "rome",
+    "Spain": "madrid",
+    "United Kingdom": "london",
+}
+
+unknown_city = df["city"] == "unknown"
+df.loc[unknown_city, "city"] = df.loc[unknown_city, "country"].map(dict_capitals)
+
+dict_cities = df.loc[df['country'].notnull(), ['city', 'country']].set_index('city').to_dict()['country']
+
 dict_cities.update(
     {
         "bristol": "United Kingdom",
@@ -7,4 +43,6 @@
         "murcia": "Spain",
         "parma": "Italy",
     },
-)
+)
+
+print("dict_cities.update(\n{\n\t\"bristol\": \"United Kingdom\",\n\t\"gothenburg\": \"Sweden\",\n\t\"graz\": \"Austria\",\n\t\"lyon\": \"France\",\n\t \"murcia\": \"Spain\",\n\t \"parma\": \"Italy\",\n},\n)\n")

content/solutions/05_32.py

@@ -1,2 +1,52 @@
+import pandas as pd
+
+df_2014 = pd.read_csv("../data/food_training/training_2014.csv", header=1)
+df_2015 = pd.read_csv("../data/food_training/training_2015.csv", header=1)
+df_2016 = pd.read_csv("../data/food_training/training_2016.csv", header=1)
+
+frames = [df_2014, df_2015, df_2016]
+df = pd.concat(frames)
+
+df = df.reset_index()
+df.index
+
+cols_to_remove = ["Unnamed: 5", "Unnamed: 6"]
+df = df.drop(cols_to_remove, axis=1)
+
+df[["city", "country"]] = df["Location"].str.split(pat=";", expand=True)
+
+df = df.drop("Location", axis=1)
+
+df["city"] = df["city"].str.lower()
+
+df["city"] = df["city"].str.replace(r"/\w*", "", regex=True)
+
+dict_capitals = {
+    "Denmark": "copenhague",
+    "France": "paris",
+    "Italy": "rome",
+    "Spain": "madrid",
+    "United Kingdom": "london",
+}
+
+unknown_city = df["city"] == "unknown"
+df.loc[unknown_city, "city"] = df.loc[unknown_city, "country"].map(dict_capitals)
+
+dict_cities = df.loc[df['country'].notnull(), ['city', 'country']].set_index('city').to_dict()['country']
+
+dict_cities.update(
+    {
+        "bristol": "United Kingdom",
+        "gothenburg": "Sweden",
+        "graz": "Austria",
+        "lyon": "France",
+        "murcia": "Spain",
+        "parma": "Italy",
+    },
+)
+
 null_country = df["country"].isnull()
-df.loc[null_country, "country"] = df.loc[null_country, "city"].map(dict_cities)
+df.loc[null_country, "country"] = df.loc[null_country, "city"].map(dict_cities)
+
+print('null_country = df["country"].isnull()')
+print('df.loc[null_country, "country"] = df.loc[null_country, "city"].map(dict_cities)')

content/solutions/05_33.py

@@ -1 +1,53 @@
-df["country"].value_counts(dropna=False)
+import pandas as pd
+
+df_2014 = pd.read_csv("../data/food_training/training_2014.csv", header=1)
+df_2015 = pd.read_csv("../data/food_training/training_2015.csv", header=1)
+df_2016 = pd.read_csv("../data/food_training/training_2016.csv", header=1)
+
+frames = [df_2014, df_2015, df_2016]
+df = pd.concat(frames)
+
+df = df.reset_index()
+df.index
+
+cols_to_remove = ["Unnamed: 5", "Unnamed: 6"]
+df = df.drop(cols_to_remove, axis=1)
+
+df[["city", "country"]] = df["Location"].str.split(pat=";", expand=True)
+
+df = df.drop("Location", axis=1)
+
+df["city"] = df["city"].str.lower()
+
+df["city"] = df["city"].str.replace(r"/\w*", "", regex=True)
+
+dict_capitals = {
+    "Denmark": "copenhague",
+    "France": "paris",
+    "Italy": "rome",
+    "Spain": "madrid",
+    "United Kingdom": "london",
+}
+
+unknown_city = df["city"] == "unknown"
+df.loc[unknown_city, "city"] = df.loc[unknown_city, "country"].map(dict_capitals)
+
+dict_cities = df.loc[df['country'].notnull(), ['city', 'country']].set_index('city').to_dict()['country']
+
+dict_cities.update(
+    {
+        "bristol": "United Kingdom",
+        "gothenburg": "Sweden",
+        "graz": "Austria",
+        "lyon": "France",
+        "murcia": "Spain",
+        "parma": "Italy",
+    },
+)
+
+null_country = df["country"].isnull()
+df.loc[null_country, "country"] = df.loc[null_country, "city"].map(dict_cities)
+
+print('df["country"].value_counts(dropna=False)\n')
+
+display(df["country"].value_counts(dropna=False))

content/solutions/05_34.py

@@ -2,4 +2,12 @@ def f(x):
     if x == 1:
         return "single"
     else:
-        return "multiple"
+        return "multiple"
+
+print('def f(x):')
+print('\tif x == 1:')
+print('\t\treturn "single"')
+print('\telse:')
+print('\t\treturn "multiple"\n')
+
+print('f(4) = ',f(4)) #x = 4