Data Operations

Data operations

This is a data operations tutorial.

Within this tutorial, data operations refers to the process of element-wise operations. Element-wise operations in Pandas involve performing operations on individual elements of a DataFrame or Series. For instance, adding or multiplying two dataframes. Operators play an important part in element-wise operations.

We cover some of the most common arithmetic and comparison methods. Let’s look at some examples of element-wise operations with scalar values.

Arithmetic operations

Arithmetic operators deal with processes such as addition, subtraction, multiplication, and division.

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3],
                   'B': [4, 5, 6]})

# Addition
df_add = df + 7
print(df_add)

# Subtraction
df_subtract = df - 3
print(df_subtract)

# Multiplication
df_multiply = df * 3
print(df_multiply)

# Division
df_divide = df / 4
print(df_divide)

    A   B
0   8  11
1   9  12
2  10  13

   A  B
0 -2  1
1 -1  2
2  0  3

   A   B
0  3  12
1  6  15
2  9  18

      A     B
0  0.25  1.00
1  0.50  1.25
2  0.75  1.50

Comparison operations

Comparison operators deal with comparing values. This method returns Boolean output (i.e. True or False).

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3],
                   'B': [4, 5, 6]})

# Greater than
df_gt = df > 2
print(df_gt)

# Less than
df_lt = df < 3
print(df_lt)

# Equality
df_eq = df == 2
print(df_eq)

       A     B
0  False  True
1  False  True
2   True  True

       A      B
0   True  False
1   True  False
2  False  False

       A      B
0  False  False
1   True  False
2  False  False

Element-wise operations

In this section, we cover element-wise operations of two series or dataframes. This method follows the same logic as with a scalar value. All methods work on both elements within a dataframe as well as two separate dataframes.

Same dataframe

import pandas as pd

# Sample DataFrame
data = {'A': [1, 2, 3],
        'B': [4, 5, 6]}
df = pd.DataFrame(data)

# Element-wise operation (addition)
result = df['A'] + df['B']
print(result)

0    5
1    7
2    9
dtype: int64

Separate dataframes

import pandas as pd

# Sample DataFrame
data_1 = {'A': [1, 2, 3],
        'B': [4, 5, 6]}

df_1 = pd.DataFrame(data_1)

# Sample DataFrame
data_2 = {'A': [3, 5, 7],
        'B': [10, 15, 20]}

df_2 = pd.DataFrame(data_2)

# Element-wise operation (addition)
result = df_1 + df_2
print(result)

    A   B
0   4  14
1   7  20
2  10  26

Applying functions

Pandas provides efficient ways of applying functions to data. There are a two different methods of applying operations, “apply()” and “map()“.

Let’s explore both techniques separately.

Apply()

This method allows you to apply a function along the axis of a dataframe or series. This means you can apply functions row-wise or column-wise. We use the common “sum” function in this scenario.

import pandas as pd

# Sample DataFrame
data = {'A': [1, 2, 3],
        'B': [4, 5, 6]}
df = pd.DataFrame(data)

# Column-wise (sum function)
column_sum = df.apply(sum, axis=0)
print(column_sum)

# Row-wise (sum functions)
row_sum = df.apply(sum, axis=1)
print(row_sum)

A     6
B    15
dtype: int64

0    5
1    7
2    9
dtype: int64

Map()

This method applies a function to every item of a dataframe. The function accepts and returns a scalar. We use the combination of map and lambda in this example.

import pandas as pd

# Sample Series
s = pd.Series([1, 2, 3, 4])

# Map & lambda functions
s_mapped = s.map(lambda x: x ** 2)

print(s_mapped)

0     1
1     4
2     9
3    16
dtype: int64

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This is an original data operations educational material created by aicorr.com.

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