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    Convert Pandas DataFrame to NumPy Array

    Convert Pandas DataFrame to NumPy Array

    You can convert pandas DataFrame to NumPy array by using to_numpy(), to_records(), index(), and values() methods. In this article, I will explain how to convert DataFrame (all or selected multiple columns) to NumPy array with examples.

    Key Points –

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    Quick Examples of Converting DataFrame to Array

    Following are quick examples of converting Pandas DataFrame to NumPy array.

    
# Quick examples of convert DataFrame to NumPy array

# Using df.to_numpy() method
result = df.to_numpy()

# Convert specific column to numpy array
df2=df['Courses'].to_numpy()

# Convert specific columns 
# Using df.to_numpy() method
df2 = df[['Courses', 'Duration']].to_numpy()

# Using DataFrame.to_records()
print(df.to_records())

# Convert Pandas DataFrame 
# To numpy array by df.Values()
values_array = df.values
print(values_array)

# Convert row index method
df.index.to_numpy()

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    Usage of Convert Pandas DataFrame to NumPy Array

    You can convert a Pandas DataFrame to a NumPy array using the values attribute or the to_numpy() method.

    To run some examples of converting pandas DataFrame to NumPy array, let’s create Pandas DataFrame using data from a dictionary.

    
import pandas as pd
import numpy as np
technologies = {
    'Courses':["Spark","PySpark","Python","pandas"],
    'Fee' :[20000,25000,22000,30000],
    'Duration':['30days','40days','35days','50days'],
    'Discount':[1000,2300,1200,2000]
              }
index_labels=['r1','r2','r3','r4']
df = pd.DataFrame(technologies,index=index_labels)
print(df)

    Yields below output.

    
    Courses    Fee Duration  Discount
r1    Spark  20000   30days      1000
r2  PySpark  25000   40days      2300
r3   Python  22000   35days      1200
r4   pandas  30000   50days      2000

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    Convert DataFrame to Array

    You can convert pandas DataFrame to NumPy array by using to_numpy() method. This method is called on the DataFrame object and returns an object of type Numpy ndarray and it accepts three optional parameters.

    For Example-

    
# Using df.to_numpy() method 
# To concert all columsn to numpy array
result = df.to_numpy()
print(result)

# Output
#[['Spark' 20000 '30days' 1000]
# ['PySpark' 25000 '40days' 2300]
# ['Python' 22000 '35days' 1200]
# ['pandas' 30000 '50days' 2000]]

    Alternatively, to convert specific columns from a Pandas DataFrame to a NumPy array, you can select the columns using bracket notation [] and then use the to_numpy() function. This allows you to choose the columns you want to convert and obtain their NumPy array representation.

    
# Convert specific rows using to_numpy() method
df2=df['Courses'].to_numpy()
print(df2)

# Outputs:
# ['Spark' 'PySpark' 'Python' 'pandas']

# Convert specific columns using df.to_numpy() method
result = df[['Courses', 'Duration']].to_numpy()
print(result)

# Output:
#[['Spark' '30days']
# ['PySpark' '40days']
# ['Python' '35days']
# ['pandas' '50days']]

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    Using DataFrame.Values() Method

    In this section, you’ll convert the pandas DataFrame into a NumPy array using df.values(). The values method returns the NumPy array representation of the DataFrame. As a result, the row and column axes (labels) are not present.

    
# Convert Pandas DataFrame 
# To numpy array by df.Values() method
values_array = df.values
print(values_array)

    Yields below output.

    
[['Spark' 20000 '30days' 1000]
 ['PySpark' 25000 '40days' 2300]
 ['Python' 22000 '35days' 1200]
 ['pandas' 30000 '50days' 2000]]

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    Convert DataFrame to NumPy Array using to_records()

    In order to get the rows axis on the NumPy array from DataFrame use DataFrame.to_records() method.

    
# Using DataFrame.to_records()
print(df.to_records())

    Yields below output.

    
[('r1', 'Spark', 20000, '30days', 1000)
 ('r2', 'PySpark', 25000, '40days', 2300)
 ('r3', 'Python', 22000, '35days', 1200)
 ('r4', 'pandas', 30000, '50days', 2000)]

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    Using Index.to_numpy() to Convert Row Indices to NumPy

    Use Index.to_numpy() method to convert DataFrame row labels to NumPy array.

    
# Using DataFrame.index.to_numpy() method
df.index.to_numpy()

    Yields below output.

    
array(['r1', 'r2', 'r3', 'r4'], dtype=object)

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    Complete Example

    
import pandas as pd
import numpy as np
technologies = {
    'Courses':["Spark","PySpark","Python","pandas"],
    'Fee' :[20000,25000,22000,30000],
    'Duration':['30days','40days','35days','50days'],
    'Discount':[1000,2300,1200,2000]
              }
index_labels=['r1','r2','r3','r4']
df = pd.DataFrame(technologies,index=index_labels)
print(df)

# Using df.to_numpy() method
print(df.to_numpy())

# Convert specific columns 
# Using df.to_numpy() method
df[['Courses', 'Duration']].to_numpy()

# Using DataFrame.index method
df.index.to_numpy()

# Convert specific rows 
# Uing to_numpy() method
df2=df['Courses'].to_numpy()
print(df2)

# Using DataFrame.to_records()
print(df.to_records())

# Convert Pandas DataFrame 
# To numpy array by df.Values() method
values_array = df.values
print(values_array)

# Convert select Columns into Numpy array
Fee_array=df[['Fee']].to_numpy()
print(Fee_array)

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    Conclusion

    In conclusion, this article has converted DataFrame to an array by using to_numpy(), to_records(), index(), and values() methods. To convert the selected columns, first select the columns from DataFrame by using bracket notation [] and on the result use to_numpy() function. Also, learned how to get a row index into the array.

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