Solution for 197,262.

solution/0100-0199/0197.Rising Temperature/README.md

@@ -87,4 +87,18 @@ FROM
         ON SUBDATE(w1.recordDate, 1) = w2.recordDate AND w1.temperature > w2.temperature;
 ```
 
+### **Pandas**
+
+```python
+import pandas as pd
+
+
+def rising_temperature(weather: pd.DataFrame) -> pd.DataFrame:
+    weather.sort_values(by="recordDate", inplace=True)
+    return weather[
+        (weather.temperature.diff() > 0) & (weather.recordDate.diff().dt.days == 1)
+    ][["id"]]
+
+```
+
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solution/0100-0199/0197.Rising Temperature/README_EN.md

@@ -80,4 +80,18 @@ FROM
         ON SUBDATE(w1.recordDate, 1) = w2.recordDate AND w1.temperature > w2.temperature;
 ```
 
+### **Pandas**
+
+```python
+import pandas as pd
+
+
+def rising_temperature(weather: pd.DataFrame) -> pd.DataFrame:
+    weather.sort_values(by="recordDate", inplace=True)
+    return weather[
+        (weather.temperature.diff() > 0) & (weather.recordDate.diff().dt.days == 1)
+    ][["id"]]
+
+```
+
 <!-- tabs:end -->

solution/0100-0199/0197.Rising Temperature/Solution.py

@@ -0,0 +1,8 @@
+import pandas as pd
+
+
+def rising_temperature(weather: pd.DataFrame) -> pd.DataFrame:
+    weather.sort_values(by="recordDate", inplace=True)
+    return weather[
+        (weather.temperature.diff() > 0) & (weather.recordDate.diff().dt.days == 1)
+    ][["id"]]

solution/0200-0299/0262.Trips and Users/README.md

@@ -140,4 +140,53 @@ WHERE request_at BETWEEN '2013-10-01' AND '2013-10-03'
 GROUP BY request_at;
 ```
 
+### **Pandas**
+
+```python
+import pandas as pd
+
+
+def trips_and_users(trips: pd.DataFrame, users: pd.DataFrame) -> pd.DataFrame:
+    # 1) temporal filtering
+    trips = trips[trips["request_at"].between("2013-10-01", "2013-10-03")].rename(
+        columns={"request_at": "Day"}
+    )
+
+    # 2) filtering based not banned
+    # 2.1) mappning the column 'banned' to `client_id` and `driver_id`
+    df_client = (
+        pd.merge(trips, users, left_on="client_id", right_on="users_id", how="left")
+        .drop(["users_id", "role"], axis=1)
+        .rename(columns={"banned": "banned_client"})
+    )
+    df_driver = (
+        pd.merge(trips, users, left_on="driver_id", right_on="users_id", how="left")
+        .drop(["users_id", "role"], axis=1)
+        .rename(columns={"banned": "banned_driver"})
+    )
+    df = pd.merge(
+        df_client,
+        df_driver,
+        left_on=["id", "driver_id", "client_id", "city_id", "status", "Day"],
+        right_on=["id", "driver_id", "client_id", "city_id", "status", "Day"],
+        how="left",
+    )
+    # 2.2) filtering based on not banned
+    df = df[(df["banned_client"] == "No") & (df["banned_driver"] == "No")]
+
+    # 3) counting the cancelled and total trips per day
+    df["status_cancelled"] = df["status"].str.contains("cancelled")
+    df = df[["Day", "status_cancelled"]]
+    df = df.groupby("Day").agg(
+        {"status_cancelled": [("total_cancelled", "sum"), ("total", "count")]}
+    )
+    df.columns = df.columns.droplevel()
+    df = df.reset_index()
+
+    # 4) calculating the ratio
+    df["Cancellation Rate"] = (df["total_cancelled"] / df["total"]).round(2)
+    return df[["Day", "Cancellation Rate"]]
+
+```
+
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solution/0200-0299/0262.Trips and Users/README_EN.md

@@ -127,4 +127,53 @@ WHERE request_at BETWEEN '2013-10-01' AND '2013-10-03'
 GROUP BY request_at;
 ```
 
+### **Pandas**
+
+```python
+import pandas as pd
+
+
+def trips_and_users(trips: pd.DataFrame, users: pd.DataFrame) -> pd.DataFrame:
+    # 1) temporal filtering
+    trips = trips[trips["request_at"].between("2013-10-01", "2013-10-03")].rename(
+        columns={"request_at": "Day"}
+    )
+
+    # 2) filtering based not banned
+    # 2.1) mappning the column 'banned' to `client_id` and `driver_id`
+    df_client = (
+        pd.merge(trips, users, left_on="client_id", right_on="users_id", how="left")
+        .drop(["users_id", "role"], axis=1)
+        .rename(columns={"banned": "banned_client"})
+    )
+    df_driver = (
+        pd.merge(trips, users, left_on="driver_id", right_on="users_id", how="left")
+        .drop(["users_id", "role"], axis=1)
+        .rename(columns={"banned": "banned_driver"})
+    )
+    df = pd.merge(
+        df_client,
+        df_driver,
+        left_on=["id", "driver_id", "client_id", "city_id", "status", "Day"],
+        right_on=["id", "driver_id", "client_id", "city_id", "status", "Day"],
+        how="left",
+    )
+    # 2.2) filtering based on not banned
+    df = df[(df["banned_client"] == "No") & (df["banned_driver"] == "No")]
+
+    # 3) counting the cancelled and total trips per day
+    df["status_cancelled"] = df["status"].str.contains("cancelled")
+    df = df[["Day", "status_cancelled"]]
+    df = df.groupby("Day").agg(
+        {"status_cancelled": [("total_cancelled", "sum"), ("total", "count")]}
+    )
+    df.columns = df.columns.droplevel()
+    df = df.reset_index()
+
+    # 4) calculating the ratio
+    df["Cancellation Rate"] = (df["total_cancelled"] / df["total"]).round(2)
+    return df[["Day", "Cancellation Rate"]]
+
+```
+
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solution/0200-0299/0262.Trips and Users/Solution.py

@@ -0,0 +1,43 @@
+import pandas as pd
+
+
+def trips_and_users(trips: pd.DataFrame, users: pd.DataFrame) -> pd.DataFrame:
+    # 1) temporal filtering
+    trips = trips[trips["request_at"].between("2013-10-01", "2013-10-03")].rename(
+        columns={"request_at": "Day"}
+    )
+
+    # 2) filtering based not banned
+    # 2.1) mappning the column 'banned' to `client_id` and `driver_id`
+    df_client = (
+        pd.merge(trips, users, left_on="client_id", right_on="users_id", how="left")
+        .drop(["users_id", "role"], axis=1)
+        .rename(columns={"banned": "banned_client"})
+    )
+    df_driver = (
+        pd.merge(trips, users, left_on="driver_id", right_on="users_id", how="left")
+        .drop(["users_id", "role"], axis=1)
+        .rename(columns={"banned": "banned_driver"})
+    )
+    df = pd.merge(
+        df_client,
+        df_driver,
+        left_on=["id", "driver_id", "client_id", "city_id", "status", "Day"],
+        right_on=["id", "driver_id", "client_id", "city_id", "status", "Day"],
+        how="left",
+    )
+    # 2.2) filtering based on not banned
+    df = df[(df["banned_client"] == "No") & (df["banned_driver"] == "No")]
+
+    # 3) counting the cancelled and total trips per day
+    df["status_cancelled"] = df["status"].str.contains("cancelled")
+    df = df[["Day", "status_cancelled"]]
+    df = df.groupby("Day").agg(
+        {"status_cancelled": [("total_cancelled", "sum"), ("total", "count")]}
+    )
+    df.columns = df.columns.droplevel()
+    df = df.reset_index()
+
+    # 4) calculating the ratio
+    df["Cancellation Rate"] = (df["total_cancelled"] / df["total"]).round(2)
+    return df[["Day", "Cancellation Rate"]]