How do you load only a subset of the rows?

When you load your data, you can specify a skiprows function that will randomly decide whether to load that row or not:

```

from random import random

def sample(row_number): ... if row_number == 0: ... # Never drop the row with column names: ... return False ... # random() returns uniform numbers between 0 and 1: ... return random() > 0.001 ... sampled = pd.read_csv("/tmp/voting.csv", skiprows=sample) len(sampled) 973 ```

If parts of your data don’t impact your analysis, no need to waste memory keeping extraneous details around.

Pandas DataFrame allows for multiple sorting

با پانداس میاد میخونه

خیلی راحت head می خونه.

معرفی کرد Feature و Label خودشا

How to display Bollinger bands with Pandas.

با استفاده از این تابع میشه برای ستون های عددی مقدار Count و Avg و غیره را بدست آورد

تعداد ردیف و ستون اون Data Frame را برمیگردونه.

5 تا ردیف اول را برات بر میگردونه. یه ورودی هم شاید بگیره که در واقع تعداد ردیف هایی است که میخواد برگردونه

یکی از کتاب خونه های خوبه Python.

Máscaras em Pandas são uma maneira de selecionar um subconjunto de dados de um DataFrame, Series ou outro objeto de dados baseado em uma condição booleana.

O código que deve ser adicionado no lugar de # a fazer é:

ff = ef[['x', 'y']]

Isso irá selecionar apenas as colunas 'x' e 'y' do DataFrame ef, que é o resultado da máscara m. A máscara m seleciona apenas as linhas onde o valor da coluna 'z' é False, e então, ef contém apenas essas linhas. Finalmente, ff é criado selecionando as colunas 'x' e 'y' do DataFrame ef.

Pandas usually writes code in this syntax:

1. JOIN
2. WHERE
3. GROUP BY
4. HAVING

Example:

1. df = thing1.join(thing2) # like a JOIN
2. df = df[df.created_at > 1000] # like a WHERE
3. df = df.groupby('something', num_yes = ('yes', 'sum')) # like a GROUP BY
4. df = df[df.num_yes > 2] # like a HAVING, filtering on the result of a GROUP BY
5. df = df[['num_yes', 'something1', 'something']] # pick the columns I want to display, like a SELECT
6. df.sort_values('sometthing', ascending=True)[:30] # ORDER BY and LIMIT
7. df[:30]

This is only true when using the Python parsing engine.

Filled 3 NA values in column name

If using the C parsing engine you get something like the following output:

Tokenization took: 0.01 ms
Type conversion took: 0.70 ms
Parser memory cleanup took: 0.01 ms

dummy encoding

a more constrained. and thus more efficient way to do transformations on groupbys than the apply method. You can do very cool stuff with it. For those of you who know splunk - this has the neat "streamstats" and "eventstats" capabilities