Pandas Data Manipulation: Sorting, Selecting, Grouping, and Reshaping

Pandas is a common tool for data mining; mastering its functions is essential. This guide demonstrates various Pandas operations through visual examples.

sort_values

(dogs[dogs['size'] == 'medium']
  .sort_values('type')
  .groupby('type').median())

Execution steps:

Filter rows where the size column equals "medium".

Convert the type column as needed.

Group by type and compute the median.

selecting a column

dogs['longevity']

groupby + mean

dogs.groupby('size').mean()

Execution steps:

Group data by the size column.

Perform aggregation (mean) within each group.

grouping multiple columns

dogs.groupby(['type', 'size'])

groupby + multi aggregation

(dogs
  .sort_values('size')
  .groupby('size')['height']
  .agg(['sum', 'mean', 'std']))

Execution steps:

Sort data by the size column.

Group by size.

Calculate sum, mean, and standard deviation of height within each group.

filtering for columns

df.loc[:, df.loc['two'] <= 20]

filtering for rows

dogs.loc[(dogs['size'] == 'medium') & (dogs['longevity'] > 12), 'breed']

dropping columns

dogs.drop(columns=['type'])

joining

ppl.join(dogs)

merging

ppl.merge(dogs, left_on='likes', right_on='breed', how='left')

pivot table

dogs.pivot_table(index='size', columns='kids', values='price')

melting

dogs.melt()

pivoting

dogs.pivot(index='size', columns='kids')

stacking column index

dogs.stack()

unstacking row index

dogs.unstack()

resetting index

dogs.reset_index()

setting index

dogs.set_index('breed')

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