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

<code>(dogs[dogs['size'] == 'medium']
  .sort_values('type')
  .groupby('type').median())
</code>

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

<code>dogs['longevity']
</code>

groupby + mean

<code>dogs.groupby('size').mean()
</code>

Execution steps:

Group data by the size column.

Perform aggregation (mean) within each group.

grouping multiple columns

<code>dogs.groupby(['type', 'size'])
</code>

groupby + multi aggregation

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

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

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

filtering for rows

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

dropping columns

<code>dogs.drop(columns=['type'])
</code>

joining

<code>ppl.join(dogs)
</code>

merging

<code>ppl.merge(dogs, left_on='likes', right_on='breed', how='left')
</code>

pivot table

<code>dogs.pivot_table(index='size', columns='kids', values='price')
</code>

melting

<code>dogs.melt()
</code>

pivoting

<code>dogs.pivot(index='size', columns='kids')
</code>

stacking column index

<code>dogs.stack()
</code>

unstacking row index

<code>dogs.unstack()
</code>

resetting index

<code>dogs.reset_index()
</code>

setting index

<code>dogs.set_index('breed')
</code>

The article concludes with a brief disclaimer and a call‑to‑action to read the original source.