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feat: add pandas solutions to lc problems: No.0197,0262 (doocs#1876)
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‎solution/0100-0199/0197.Rising Temperature/README.md

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

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

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

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

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

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

‎solution/0200-0299/0262.Trips and Users/Solution.py

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

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