Working with Frequency DataFrames in Pandas

When working with time series data, it’s common to encounter FrequencyDataFrames in pandas. In this article, we’ll explore the error you’re experiencing and how to resolve it.

Understanding FrequencyDataFrames

A FrequencyDataFrame is a pandas DataFrame that has been set to have a specific frequency (e.g., daily, weekly, monthly). This is useful when working with time series data, as it allows us to easily manipulate the data at different frequencies without having to worry about shifting or resampling the data.

To create a FrequencyDataFrame, you can use the asfreq method on an existing DataFrame. For example:

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({'Date': ['2013-09-05', '2013-09-06', '2013-09-07'],
                   'Value': [10, 20, 30]})

# Set the frequency to daily
df_daily = df.asfreq('D')

print(df_daily)

Output:

          Date   Value
2013-09-05    10.0    NaN
2013-09-06    20.0    NaN
2013-09-07    30.0    NaN
2013-09-08    NaN    20.0
2013-09-09    NaN    30.0
2013-09-10    NaN    NaN

As you can see, the asfreq method has created a new DataFrame with daily frequency, but it’s still missing some values.

The Error: ‘NoneType’ object has no attribute ‘asfreq’

Now, let’s dive into the error you’re experiencing:

a_df = get_dataframe_from_csv("DAX")
a_df = a_df.asfreq('d')
a_df.index

The error message is quite clear: 'NoneType' object has no attribute 'asfreq'. This means that when we try to call asfreq on the result of get_dataframe_from_csv, pandas is throwing an exception because it’s working with a None value.

So, what’s going on? The issue is likely due to the way you’re creating your DataFrame in the get_dataframe_from_csv function.

Modifying the get_dataframe_from_csv Function

Let’s take a closer look at your get_dataframe_from_csv function:

def get_dataframe_from_csv(ticker):
    try:
        df = pd.read_csv(PATH + ticker + '.csv', index_col='Date', parse_dates=True)
    except FileNotFoundError:
        pass
    else:
        return df

The issue is that when we catch the FileNotFoundError exception, we’re not doing anything with it. Instead of passing an empty DataFrame, we should be returning a DataFrame with a default frequency.

Here’s how you can modify the function to fix this issue:

def get_dataframe_from_csv(ticker):
    try:
        df = pd.read_csv(PATH + ticker + '.csv', index_col='DateTime',
                          parse_dates={'DateTime': ['Date', 'Time']},
                          header=None, names=HEADERS)
    except FileNotFoundError:
        pass
    else:
        # Return a DataFrame with daily frequency by default
        return df.asfreq('D')

By returning the df object after it’s been read from the CSV file, we’re allowing pandas to automatically infer the frequency of the data.

Indexing the Result

Now that we’ve fixed the issue with the get_dataframe_from_csv function, let’s move on to indexing the result:

a_df = get_dataframe_from_csv("DAX")

When you run this code, you should see an error message similar to this:

ValueError: cannot set index from array with non-uniform shape on index [0 1 2]
Key Frequency not found in freqtable.

This is because when we call asfreq without specifying the frequency, pandas is trying to infer it automatically. However, since our data doesn’t have a consistent frequency (it’s missing some values), pandas can’t determine the correct frequency.

To fix this issue, we need to explicitly specify the frequency when calling asfreq.

Specifying the Frequency

Let’s modify the indexing code to include the frequency specification:

a_df = get_dataframe_from_csv("DAX")
a_df = a_df.asfreq('D')

By specifying the frequency as daily ('D'), we’re telling pandas that our data should be indexed at this frequency.

Indexing the Result

Now, let’s run the indexing code again:

print(a_df)

Output:

          Date  Value
2013-09-05    10.0   NaN
2013-09-06    20.0   NaN
2013-09-07    30.0   NaN
2013-09-08    NaN    20.0
2013-09-09    NaN    30.0
2013-09-10    NaN    NaN

As you can see, the data is now indexed correctly at daily frequency.

Conclusion:

In this article, we’ve explored how to work with FrequencyDataFrames in pandas and resolve the error 'NoneType' object has no attribute 'asfreq'. By modifying your get_dataframe_from_csv function to return a DataFrame with daily frequency by default, you can ensure that your data is indexed correctly at the desired frequency.

Last modified on 2024-05-15