Resolving TypeError: No Numeric Data to Plot When Numeric Data Exists in Python

Encountering the error “TypeError: no numeric data to plot” in Python can be frustrating, especially when you know numeric data exists in your DataFrame. This error typically occurs when the data you intend to plot is not recognized as numeric due to incorrect data types, such as strings instead of integers or floats. Common scenarios include reading data from CSV files where numeric columns are mistakenly interpreted as strings. This error can significantly impact data visualization tasks by preventing the creation of plots, thus hindering data analysis and insights.

Understanding the Error

The error TypeError: no numeric data to plot in Python typically occurs when you attempt to plot data from a pandas DataFrame, but the data is not recognized as numeric. Here are the main causes and how data types in pandas DataFrames can lead to this error:

1. Non-Numeric Data Types:

   • String Representation of Numbers: Columns that appear numeric but are stored as strings (e.g., ‘5′, ‘10′) will cause this error. Even though they look like numbers, pandas treats them as objects (strings) and not numeric types.
   • Mixed Data Types: Columns containing a mix of numeric and non-numeric data (e.g., numbers and text) will be treated as objects.

2. Empty DataFrame:

   • Attempting to plot an empty DataFrame or a DataFrame with no numeric columns will also trigger this error.

3. Incorrect Data Type Conversion:

   • If numeric data is not properly converted to a numeric type (e.g., using astype(float) or pd.to_numeric()), pandas will not recognize it as numeric.

Example Scenario

Consider a DataFrame where numeric values are stored as strings:

import pandas as pd

df = pd.DataFrame({
    'year': [2011, 2014, 2018, 2012, 2025],
    'net_worth': ['5000', '10000', '15000', '10000', '20000'],
    'gdp_per_capita': ['10000', '12000', '15000', '20000', '25000']
})

# Attempting to plot will raise the error
df[['net_worth', 'gdp_per_capita']].plot()  # TypeError: no numeric data to plot

Solution

Convert the columns to numeric types:

df['net_worth'] = df['net_worth'].astype(float)
df['gdp_per_capita'] = df['gdp_per_capita'].astype(float)

# Now plotting will work
df[['net_worth', 'gdp_per_capita']].plot()

By ensuring that all columns intended for plotting are of a numeric type, you can avoid this error.

Identifying Non-Numeric Data

1. Check Data Types:

   import pandas as pd
   
   # Sample DataFrame
   df = pd.DataFrame({
       'team': ['A', 'A', 'B', 'B', 'B'],
       'points': ['5', '7', '7', '9', '12'],
       'rebounds': ['11', '8', '10', '6', '6'],
       'blocks': ['4', '7', '7', '6', '5']
   })
   
   # Check data types
   print(df.dtypes)
   

2. Identify Non-Numeric Columns:

   non_numeric_columns = df.select_dtypes(exclude=['number']).columns
   print(non_numeric_columns)
   

3. Convert Columns to Numeric:

   df['points'] = pd.to_numeric(df['points'], errors='coerce')
   df['rebounds'] = pd.to_numeric(df['rebounds'], errors='coerce')
   df['blocks'] = pd.to_numeric(df['blocks'], errors='coerce')
   
   # Verify conversion
   print(df.dtypes)
   

These steps will help you identify and convert non-numeric data in your DataFrame.

Converting Data Types

To resolve the TypeError: no numeric data to plot in Python when using pandas, you need to ensure that the columns you want to plot are of numeric types. Here’s how you can convert columns to numeric types using astype() and to_numeric():

Using astype()

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({
    'team': ['A', 'A', 'B', 'B', 'B'],
    'points': ['5', '7', '7', '9', '12'],
    'rebounds': ['11', '8', '10', '6', '6'],
    'blocks': ['4', '7', '7', '6', '5']
})

# Convert columns to numeric types
df['points'] = df['points'].astype(float)
df['rebounds'] = df['rebounds'].astype(float)
df['blocks'] = df['blocks'].astype(float)

# Verify data types
print(df.dtypes)

Using to_numeric()

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({
    'team': ['A', 'A', 'B', 'B', 'B'],
    'points': ['5', '7', '7', '9', '12'],
    'rebounds': ['11', '8', '10', '6', '6'],
    'blocks': ['4', '7', '7', '6', '5']
})

# Convert columns to numeric types
df['points'] = pd.to_numeric(df['points'])
df['rebounds'] = pd.to_numeric(df['rebounds'])
df['blocks'] = pd.to_numeric(df['blocks'])

# Verify data types
print(df.dtypes)

Both methods will convert the specified columns to numeric types, allowing you to plot the data without encountering the TypeError.

Verifying Data Conversion

To verify that data conversion has been successful in resolving the ‘TypeError: no numeric data to plot’ in Python, you can use the following methods:

1. Check Data Types Using dtypes:

   import pandas as pd
   
   df = pd.DataFrame({
       'year': [2011, 2014, 2018, 2012, 2025],
       'net_worth': ['5000', '10000', '15000', '10000', '20000'],
       'gdp_per_capita': ['10000', '12000', '15000', '20000', '25000'],
   })
   
   # Convert columns to numeric
   df['net_worth'] = df['net_worth'].astype(float)
   df['gdp_per_capita'] = df['gdp_per_capita'].astype(float)
   
   # Verify data types
   print(df.dtypes)
   

2. Use pd.to_numeric with errors='coerce':

   import pandas as pd
   
   df = pd.DataFrame({
       'year': [2011, 2014, 2018, 2012, 2025],
       'net_worth': ['5000', '10000', '15000', '10000', '20000'],
       'gdp_per_capita': ['10000', '12000', '15000', '20000', '25000'],
   })
   
   # Convert columns to numeric
   df['net_worth'] = pd.to_numeric(df['net_worth'], errors='coerce')
   df['gdp_per_capita'] = pd.to_numeric(df['gdp_per_capita'], errors='coerce')
   
   # Verify data types
   print(df.dtypes)
   

3. Check for Non-Numeric Values:

   import pandas as pd
   
   df = pd.DataFrame({
       'year': [2011, 2014, 2018, 2012, 2025],
       'net_worth': ['5000', '10000', '15000', '10000', '20000'],
       'gdp_per_capita': ['10000', '12000', '15000', '20000', '25000'],
   })
   
   # Convert columns to numeric
   df['net_worth'] = pd.to_numeric(df['net_worth'], errors='coerce')
   df['gdp_per_capita'] = pd.to_numeric(df['gdp_per_capita'], errors='coerce')
   
   # Check for non-numeric values
   print(df[df[['net_worth', 'gdp_per_capita']].isnull().any(axis=1)])
   

These methods ensure that your data is correctly converted to numeric types and ready for plotting.

Plotting Numeric Data

To resolve the TypeError: no numeric data to plot in Python and plot your data, follow these steps:

1. Ensure Data is Numeric: Convert your DataFrame columns to numeric types using astype() or to_numeric().

2. Plot the Data: Use matplotlib or pandas plotting functions.

Here’s a sample code:

import pandas as pd
import matplotlib.pyplot as plt

# Sample DataFrame with non-numeric data
df = pd.DataFrame({
    'year': [2011, 2014, 2018, 2012, 2025],
    'net_worth': ['5000', '10000', '15000', '10000', '20000'],
    'gdp_per_capita': ['10000', '12000', '15000', '20000', '25000']
})

# Convert columns to numeric
df['net_worth'] = df['net_worth'].astype(float)
df['gdp_per_capita'] = df['gdp_per_capita'].astype(float)

# Plotting using pandas
df.plot(x='year', y=['net_worth', 'gdp_per_capita'], kind='line')
plt.show()

This code converts the net_worth and gdp_per_capita columns to numeric types and then plots them.

To Resolve the ‘TypeError: No Numeric Data to Plot’ Issue in Python

Follow these steps:

1. Inspect Data Types: Verify that your DataFrame columns are indeed numeric by using the `dtypes` attribute or the `pd.to_numeric()` function with errors=’coerce’.
2. Convert Non-Numeric Values: Use the `pd.to_numeric()` function to convert non-numeric values in your DataFrame columns to NaN (Not a Number) or another suitable numeric type.
3. Check for Missing Values: Identify and handle missing values using the `isnull()` method, which returns a boolean mask indicating whether each value is null or not.
4. Plotting with Correct Data Types: Once you’ve ensured that your data is correctly converted to numeric types, use plotting functions like `matplotlib` or `pandas` built-in plotting capabilities.

Data type management is crucial in pandas for successful data visualization. Failing to convert non-numeric values can lead to errors and incorrect results. By following these steps, you’ll be able to resolve the ‘TypeError: no numeric data to plot’ issue and create accurate visualizations of your data.