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