Here’s an explanation of the provided code, including how it works and what each part accomplishes:

Overview

The code creates a new dataframe df2 that contains the time instances for each instance (instnceId) on each date. It then merges this new dataframe with another dataframe df, which contains the original data.

Step 1: Generating df2

In this step, we use the pd.merge function to create a new dataframe df2. The merge is done on two conditions:

• For each time in instance (instnceId and timestamp)
• For each date (creTimestamp.dt.date.drop_duplicates())

Here’s how it works:

1. We first assign the time of day from df.creTimestamp to a new column called timestamp.
2. We drop duplicate rows based on instnceId and timestamp, so we don’t have multiple instances with the same ID and time.
3. We then create a new dataframe that contains all dates, one for each date in df.creTimestamp. This is done by using the dt.date accessor to extract the date from creTimestamp.
4. We merge these two dataframes on the condition that they have the same ID and timestamp.

Step 2: Merging Values

In this step, we use the merge function again to fill in missing values (NaN) in df2.

Here’s how it works:

1. We first find the last non-missing value of each column in df2 for rows where there is a missing value.
2. We then merge this new dataframe with df2 on the condition that they have the same timestamp and instance ID.
3. We use the fillna function to fill in missing values (NaN) with the last non-missing value.

Step 3: Result

The resulting dataframe df2 contains all time instances for each instance (instnceId) on each date, along with the filled-in missing values.

Here’s a summary of how you can apply this code:

1. Install the pandas library if not already installed.
2. Import the necessary libraries and load your data into dataframes df and df2.
3. Run the provided code to create the dataframe df2.
4. You now have a new dataframe that contains all time instances for each instance (instnceId) on each date, with missing values filled in.

Here’s an example of how you can use this code:

import pandas as pd

# assume 'df' and 'df2' are dataframes containing your data
df = pd.DataFrame({
    "instnceId": ["A", "B", "C"],
    "timestamp": ["12:00", "13:00", "14:00"],
    "creTimestamp": ["2021-01-22 12:00", "2021-01-23 13:00", "2021-01-24 14:00"]
})

df2 = (pd.merge(
    df.assign(timestamp=df.creTimestamp.dt.time).drop_duplicates(),
    df.creTimestamp.dt.date.drop_duplicates().to_frame().assign(foo=1),
    on="foo"
).assign(creTimestamp=lambda dfa: dfa.apply(lambda r: pd.Timestamp.combine(r["creTimestamp"], r["timestamp"]), axis=1))
.assign(CPULoad=lambda dfa: dfa.CPULoad.fillna(dfa.CPULoad))

print(df2)

Last modified on 2024-09-08