CODEWITHSUNDEEP
pandasdataframegroupparking
samita
samita

Reputation: 175

How to insert value into multiple row by grouping by place and time in pandas?

This is the dataset of parking area. The dataset have different parking area with different time as well. I want to insert rate for specific parking area for different time like morning, evening and night. https://prnt.sc/5pl1usWXVZQt

I tried grouping according to area and time. Now I want to insert accordingly. for example parkingrate for FirstHill at morning 8 to 11AM is 0.5$. Can anyone help me with this? https://prnt.sc/iTohLXmkYXsd

df_parking_1 = df_parking.groupby(['PaidParkingArea','OccupancyDateTime'])

Upvotes: 0

Views: 19

Answers (1)

Laurent
Laurent

Reputation: 13488

You do not need Panda groupby to do that, here is a reproducible example:

import pandas as pd

df = pd.DataFrame(
    {
        "OccupancyDateTime": [
            "06/05/2023 04:12:00 PM",
            "06/05/2023 08:23:00 AM",
            "06/05/2023 09:15:00 AM",
            "06/05/2023 04:12:00 PM",
            "06/05/2023 10:58:00 AM",
        ],
        "PaidParkingArea": [
            "First Hill",
            "First Hill",
            "Commercial core",
            "Belltown",
            "First Hill",
        ],
    }
)

To insert a new column PaidParkingRate which value is $ 0.5 when PaidParkingArea is First Hill and OccupancyDateTime is beteween 8 AM and 11 AM, you can for instance use Pandas loc instead:

# Format column values
df["OccupancyDateTime"] = pd.to_datetime(
    df["OccupancyDateTime"], format="%d/%m/%Y %I:%M:%S %p"
)

# Set new values
df.loc[
    (df["OccupancyDateTime"].dt.hour >= 8)
    & (df["OccupancyDateTime"].dt.hour < 11)
    & (df["PaidParkingArea"] == "First Hill"),
    "PaidParkingRate",
] = 0.5

Then:

print(df)
# Output

    OccupancyDateTime  PaidParkingArea  PaidParkingRate
0 2023-05-06 16:12:00       First Hill              NaN
1 2023-05-06 08:23:00       First Hill              0.5
2 2023-05-06 09:15:00  Commercial core              NaN
3 2023-05-06 16:12:00         Belltown              NaN
4 2023-05-06 10:58:00       First Hill              0.5

Upvotes: 0

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