# 5 Best Ways to Convert Pandas Period to Desired Frequency

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π‘ Problem Formulation: When working with time series data in pandas, we often encounter the need to adjust the sampling of periods to a different frequency. A developer may start with data in a monthly period but require a quarterly period, or vice-versa. For instance, transforming a pandas PeriodIndex from ‘M’ (monthly) to ‘Q’ (quarterly) requires proper period conversion techniques. This article walks through five effective methods to accomplish this task.

## Method 1: Using `asfreq()` method

The `asfreq()` method in pandas is designed to convert a Period or PeriodIndex to the specified frequency. This method is useful for resampling time-series data and is straightforward to use. It is capable of handling frequency conversion both to higher and lower frequencies. The method accepts the ‘freq’ argument that specifies the target frequency.

Here’s an example:

```import pandas as pd

# Create a monthly period
monthly_period = pd.Period('2023-01', freq='M')

# Convert to a quarterly period
quarterly_period = monthly_period.asfreq('Q-DEC')

print(quarterly_period)```

Output:

`2023Q1`

In this example, we convert a monthly period (‘2023-01’) to a quarterly period using the `asfreq()` method. The ‘Q-DEC’ argument defines that the quarterly frequency should end in December, resulting in ‘2023Q1’ being the new frequency which represents the first quarter of the year 2023.

## Method 2: Using `to_period()` DataFrame Method

When dealing with a DataFrame with a DateTimeIndex, the `to_period()` method can convert the entire index to the desired period frequency. This is particularly useful for converting time stamps to periods of a specific frequency and can be applied directly to the DataFrame.

Here’s an example:

```import pandas as pd

# Create a DataFrame with DateTimeIndex
date_range = pd.date_range(start='2023-01-01', end='2023-03-31', freq='M')
df = pd.DataFrame(index=date_range, data={'Value': [100, 200, 300]})

# Convert the DateTimeIndex to quarterly PeriodIndex
df.index = df.index.to_period('Q')

print(df)```

Output:

```        Value
2023Q1    100
2023Q1    200
2023Q1    300```

In the example, we create a DataFrame with a monthly DateTimeIndex and then use the `to_period()` method on the index to convert it to a quarterly PeriodIndex. The resulting DataFrame shows the ‘Value’ column associated with the new PeriodIndex of ‘2023Q1’ for all rows.

## Method 3: Direct Assignment Using `PeriodIndex`

Pandas provides a `PeriodIndex` constructor for creating a period index with a specified frequency. You can replace the index of an existing DataFrame with a new PeriodIndex with the desired frequency using direct assignment.

Here’s an example:

```import pandas as pd

# Create a monthly period DataFrame
dates = pd.date_range('2023-01', periods=3, freq='M')
data = {'value': [10, 20, 30]}
df = pd.DataFrame(data, index=pd.PeriodIndex(dates, freq='M'))

# Change frequency from M to Q
df.index = pd.PeriodIndex(df.index, freq='Q')

print(df)```

Output:

```        value
2023Q1     10
2023Q1     20
2023Q1     30```

Here, we used `PeriodIndex` to first create a DataFrame with monthly frequency and then reassigned the index with a quarterly frequency ‘Q’, which directly changed the DataFrame’s period without modifying the data.

## Method 4: Using a Lambda Function with `apply()`

The `apply()` method combined with a lambda function provides a flexible way to apply any function to all elements of a DataFrame or Series. This can be used to adjust the period of individual entries according to custom logic if needed.

Here’s an example:

```import pandas as pd

# Create a PeriodIndex with monthly frequency
months = pd.PeriodIndex(['2023-01', '2023-02', '2023-03'], freq='M')

# Convert each period to quarterly using a lambda function
quarters = months.map(lambda x: x.asfreq('Q'))

print(quarters)```

Output:

`PeriodIndex(['2023Q1', '2023Q1', '2023Q1'], dtype='period[Q-DEC]', freq='Q-DEC')`

The application of the lambda function in this case uses `asfreq()` on each individual month in the PeriodIndex to convert them to the quarterly frequency. The map function applies this transformation to each element, resulting in a new PeriodIndex with the desired ‘Q-DEC’ frequency.

## Bonus One-Liner Method 5: Using List Comprehension

List comprehension in Python is a concise way to apply an expression to each item in a list. This technique can be used with pandas’ periods to quickly transform all periods in an iterable to another frequency.

Here’s an example:

```import pandas as pd

# Create a list of monthly periods
monthly_periods = [pd.Period('2023-01', freq='M'), pd.Period('2023-02', freq='M')]

# Convert all monthly periods to quarterly in one line
quarterly_periods = [period.asfreq('Q') for period in monthly_periods]

print(quarterly_periods)```

Output:

`[Period('2023Q1', 'Q-DEC'), Period('2023Q1', 'Q-DEC')]`

The list comprehension iterates over each monthly period in `monthly_periods`, applies `asfreq('Q')` to convert it to a quarterly period, and compiles the results into a new list, `quarterly_periods`.

## Summary/Discussion

• Method 1: `asfreq()` method. Pros: Simple and built-in. Cons: Only works on individual Period objects or PeriodIndex.
• Method 2: `to_period()` DataFrame Method. Pros: Suitable for DataFrames with DateTimeIndex. Cons: Cannot be used on Period objects directly.
• Method 3: Direct Assignment Using `PeriodIndex`. Pros: Effective for replacing entire DataFrame index. Cons: Less granular control over individual periods.
• Method 4: Using a Lambda Function with `apply()`. Pros: Flexible, allows for custom logic. Cons: Slightly more verbose, can be slower for large datasets.
• Bonus Method 5: Using List Comprehension. Pros: Quick and Pythonic for lists of period objects. Cons: Not directly applicable to panda’s DataFrames or Series without additional transformation.