How do I resample data in Python Pandas?

To resample time-series data in Python Pandas, you can use the resample() method, which is used to group the data into a specified time frequency.

Here’s an example of how to resample data in Python Pandas:

import pandas as pd

# Read CSV data into a Pandas DataFrame
df = pd.read_csv('data.csv', index_col='timestamp', parse_dates=['timestamp'])

# Resample data at daily frequency
daily_resampled_df = df.resample('D').sum()

In this example, we first read in the CSV data into a Pandas DataFrame, specifying the timestamp column as the index column and parsing it as dates using the parse_dates parameter. Then, we use the resample() method to group the data into daily frequency, using the frequency code ‘D’. Finally, we apply the sum() method to get the sum of the data over each day.

There are many other frequency codes that you can use with the resample() method, including ‘H’ for hourly, ‘M’ for monthly, and ‘Y’ for yearly frequency. You can also use custom frequency codes by specifying the frequency as a string, such as ‘5D’ for 5-day frequency or ‘10T’ for 10-minute frequency.

Once you have resampled your data, you can then perform various operations on it, such as taking the mean, maximum, or minimum for each time period.

Resampling time series with groupby() in pandas

Resampling time series with groupby() in pandas can be done by first grouping the data by a specific column containing timestamps, and then using the resample() method to change the frequency of the data. Here is an example:

import pandas as pd

# create a dataframe with a timestamp column and some data
df = pd.DataFrame({
    'timestamp': pd.date_range('2022-01-01', periods=10, freq='D'),
    'value': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
})

# group the data by month and resample to weekly frequency
grouped = df.groupby(pd.Grouper(key='timestamp', freq='M'))
resampled = grouped['value'].resample('W').sum()

print(resampled)

In this example, we first create a DataFrame with a timestamp column and some data, then group the data by month using groupby(). Next, we use resample() on the grouped data to resample the data to a weekly frequency. Finally, we sum the values together for each week using the sum() method.

Note that the Grouper function is used to specify the key on which we would like to group our data. In this example, we use the 'timestamp' column to group the data, and specify our desired frequency by passing the 'M' argument to the freq parameter of Grouper.