Python function is not implemented for this dtype: [how->mean,dtype->object] error

Did you come across the Python notimplementederror: function is not implemented for this dtype: [how->mean,dtype->object] error? In this Python tutorial, I will explain what this error is and how to handle it with some examples.

To resolve the Python function is not implemented for this dtype: [how->mean,dtype->object] error, key steps include checking data types, converting non-numeric types to numeric, using selective aggregation for appropriate columns, managing NaN values, or employing custom functions in Pandas.

What the Python function is not implemented for this dtype: [how->mean,dtype->object] error

The error message in the Python function is not implemented for this dtype: [how->mean,dtype->object] typically occurs when attempting to perform an operation that is not suitable for the data type of the object we are working with. This is a common issue in Python, especially when dealing with data manipulation using libraries like Pandas.

import pandas as pd

data = {
    'Date': ['2023-07-01', '2023-07-01', '2023-07-02'],
    'Item Description': ['Notebook', 'Pen', 'Pencil'],
    'Sales Amount': ['$10', 5, '$3']
}
sales_df = pd.DataFrame(data)
# Attempting to calculate the mean of 'Sales Amount'
try:
    print(sales_df['Sales Amount'].mean())
except Exception as e:
    print("Error:", e)

Output:

Error: can only concatenate str (not "int") to str

Below is a screenshot depicting the output, captured after the code was run in the PyCharm editor.

Python function is not implemented for this dtype: [how->mean,dtype->object]

How to Handle the Python Function is not implemented for this dtype: [how->mean,dtype->object] Error

There are five different ways to handle the Python function is not implemented for this dtype: [how->mean,dtype->object] Error:

  1. Check Data Types
  2. Convert Data Types
  3. Selective Aggregation
  4. Handling NaN Values
  5. Use Custom Functions

Let’s see them one by one using some illustrative examples:

1. Check the data to handle the function is not implemented for this dtype error

We can use the df.dtypes to inspect the data types of each column in our dataframe. This helps identify which columns are causing the issue.

import pandas as pd

data = {'Age': [25, 40, 30],
        'Appointment Date': ['2023-06-01', '2023-06-02', '2023-06-03'],
        'Diagnosis Code': ['D123', 'A456', 'B789']}

df = pd.DataFrame(data)
print(df.dtypes)

Output:

Age                  int64
Appointment Date    object
Diagnosis Code      object
dtype: object
function is not implemented for this dtype error in Python

2. Convert the data before to handle notimplementederror error

We can convert non-numeric columns to numeric where appropriate using pd.to_numeric(), astype(float), or similar functions.

We can handle non-numeric values or errors during conversion using arguments like errors=’coerce’ which replaces non-convertible values with NaN.

import pandas as pd

data = {'Age': [25, 40, 30],
        'Appointment Date': ['2023-06-01', '2023-06-02', '2023-06-03'],
        'Diagnosis Code': ['D123', 'A456', 'B789']}
df = pd.DataFrame(data)
df['Diagnosis Code'] = pd.to_numeric(df['Diagnosis Code'], errors='coerce')
print(df)

Output:

   Age Appointment Date  Diagnosis Code
0   25       2023-06-01             NaN
1   40       2023-06-02             NaN
2   30       2023-06-03             NaN
notimplementederror error in Python

3. agg function failed [how->mean,dtype->object] using selective aggregative

Apply the mean (or other functions) only to columns with numeric data types.

We can use conditional selection or the select_dtypes method to isolate numeric columns.

import pandas as pd
import numpy as np

data = {'Age': [25, 40, 30],
        'Appointment Date': ['2023-06-01', '2023-06-02', '2023-06-03'],
        'Diagnosis Code': ['D123', 'A456', 'B789']}

df = pd.DataFrame(data)

numeric_cols = df.select_dtypes(include=[np.number])
print(numeric_cols.mean())

Output:

Age    31.666667
dtype: float64
notimplementederror: function is not implemented for this dtype: [how->mean,dtype->object] error in Python

4. Handle nan values

If our object columns contain mixed types (e.g., numbers and strings) and we’ve converted them to numeric, handle NaN values that arise due to conversion.

We can use methods like fillna() or dropna() to handle NaN values.

import pandas as pd
import numpy as np

data = {'Age': [25, 40, 30],
        'Appointment Date': ['2023-06-01', '2023-06-02', '2023-06-03'],
        'Diagnosis Code': ['D123', 'A456', 'B789']}

df = pd.DataFrame(data)
df['Income'] = pd.Series([50000, np.nan, 45000])
df['Income'].fillna(df['Income'].mean(), inplace=True)
print(df)

Output:

   Age Appointment Date Diagnosis Code   Income
0   25       2023-06-01           D123  50000.0
1   40       2023-06-02           A456  47500.0
2   30       2023-06-03           B789  45000.0
python groupby without pandas

5. Handle function is not implemented for this dtype: [how->mean,dtype->object] using a custom function

For columns with mixed data types where we still need to calculate mean or other statistical functions, consider writing custom functions.

import pandas as pd

data = {'Age': [25, 40, 30],
        'Appointment Date': ['2023-06-01', '2023-06-02', '2023-06-03'],
        'Diagnosis Code': ['D123', 'A456', 'B789']}

df = pd.DataFrame(data)
def custom_mean(column):
    numeric = pd.to_numeric(column, errors='coerce').fillna(0)
    return numeric.mean()

survey_data = {'Response': ['Yes', 'No', '3', '4', '5']}
df_survey = pd.DataFrame(survey_data)
print(custom_mean(df_survey['Response']))

Output:

2.4
agg function failed [how->mean,dtype->object] in Python

Conclusion

Here, I have explained various strategies to address the Python function is not implemented for this dtype: [how->mean,dtype->object] error, a common issue in data manipulation with Pandas. These strategies include checking and converting data types, applying selective aggregation, handling NaN values, and using custom functions.

These methods are crucial for effectively managing and analyzing datasets, particularly when dealing with mixed or non-numeric data types in Python’s Pandas library.

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