Python One‑Line List Comprehensions, Lambda, Map/Filter, NumPy arange & linspace, Pandas Axis, Concat/Merge/Join, Apply, and Pivot Tables

Define a list in one line using list comprehension instead of a verbose for‑loop.

<code>x = [1,2,3,4]
out = []
for item in x:
  out.append(item**2)
print(out)
# [1, 4, 9, 16]

x = [1,2,3,4]
out = [item**2 for item in x]
print(out)
# [1, 4, 9, 16]</code>

Lambda expressions provide a concise way to create anonymous functions.

<code>lambda arguments: expression</code>

Example: a simple doubling function.

<code>double = lambda x: x * 2
print(double(5))
# 10</code>

Combine lambda with map to apply an operation to every element of a sequence.

<code># Map
seq = [1, 2, 3, 4, 5]
result = list(map(lambda var: var*2, seq))
print(result)
# [2, 4, 6, 8, 10]</code>

Use filter with a lambda to keep elements that satisfy a condition.

<code># Filter
seq = [1, 2, 3, 4, 5]
result = list(filter(lambda x: x > 2, seq))
print(result)
# [3, 4, 5]</code>

NumPy's arange creates an arithmetic sequence with a given step.

<code># np.arange(start, stop, step)
np.arange(3, 7, 2)
# array([3, 5])</code>

NumPy's linspace generates a specified number of evenly spaced points between start and stop.

<code># np.linspace(start, stop, num)
np.linspace(2.0, 3.0, num=5)
# array([2. , 2.25, 2.5 , 2.75, 3. ])</code>

In pandas, the axis parameter controls whether operations act on rows (0) or columns (1); for example, dropping a column or a row.

<code>df.drop('Column A', axis=1)
df.drop('Row A', axis=0)</code>

The shape attribute returns a tuple (rows, columns) , helping you understand the DataFrame dimensions.

<code>df.shape  # (number_of_rows, number_of_columns)</code>

DataFrames can be combined using concat (stack vertically or horizontally), merge (SQL‑style join on key columns), or join (join on index or column names).

Pandas apply lets you run a function on each element of a Series or across an axis of a DataFrame, avoiding explicit loops.

<code>df = pd.DataFrame([[4, 9],] * 3, columns=['A', 'B'])
df.apply(np.sqrt)
#      A    B
# 0  2.0  3.0
# 1  2.0  3.0
# 2  2.0  3.0

df.apply(np.sum, axis=0)
# A    12
# B    27

df.apply(np.sum, axis=1)
# 0    13
# 1    13
# 2    13</code>

Pandas also provides pivot_table to create Excel‑like pivot tables for summarizing data.

<code>pd.pivot_table(df, index=["Manager", "Rep"])
pd.pivot_table(df, index=["Manager", "Rep"], values=["Price"])</code>

These examples illustrate essential Python and pandas techniques for efficient data manipulation and analysis.