魅族mx5游戲模式小熊貓

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I’ve been using pandas for years and each time I feel I am typing too much, I google it and I usually find a new pandas trick! I learned about these functions recently and I deem them essential because of ease of use.

我已經(jīng)使用熊貓多年了，每次我輸入太多單詞時(shí)，我都會(huì)用google搜索它，而且我通常會(huì)發(fā)現(xiàn)一個(gè)新的熊貓技巧！ 我最近了解了這些功能，并且由于易于使用，我認(rèn)為它們是必不可少的。

1.功能之間 (1. between function)

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I’ve been using “between” function in SQL for years, but I only discovered it recently in pandas.

多年來(lái)，我一直在SQL中使用“ between”功能，但最近才在pandas中發(fā)現(xiàn)它。

Let’s say we have a DataFrame with prices and we would like to filter prices between 2 and 4.

假設(shè)我們有一個(gè)帶有價(jià)格的DataFrame，并且我們希望在2到4之間過(guò)濾價(jià)格。

df = pd.DataFrame({'price': [1.99, 3, 5, 0.5, 3.5, 5.5, 3.9]})

With between function, you can reduce this filter:

使用between功能，可以減少此過(guò)濾器：

df[(df.price >= 2) & (df.price <= 4)]

To this:

對(duì)此：

df[df.price.between(2, 4)]

It might not seem much, but those parentheses are annoying when writing many filters. The filter with between function is also more readable.

看起來(lái)似乎不多，但是編寫(xiě)許多過(guò)濾器時(shí)這些括號(hào)令人討厭。 具有中間功能的過(guò)濾器也更易讀。

between function sets interval left <= series <= right.

功能集之間的間隔左<=系列<=右。

2.使用重新索引功能固定行的順序 (2. Fix the order of the rows with reindex function)

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Reindex function conforms a Series or a DataFrame to a new index. I resort to the reindex function when making reports with columns that have a predefined order.

Reindex函數(shù)使Series或DataFrame符合新索引。 當(dāng)使用具有預(yù)定義順序的列制作報(bào)表時(shí)，我求助于reindex函數(shù)。

Let’s add sizes of T-shirts to our Dataframe. The goal of analysis is to calculate the mean price for each size:

讓我們?cè)跀?shù)據(jù)框中添加T恤的尺寸。 分析的目的是計(jì)算每種尺寸的平ASP格：

df = pd.DataFrame({'price': [1.99, 3, 5], 'size': ['medium', 'large', 'small']})df_avg = df.groupby('size').price.mean()
df_avg

Sizes have a random order in the table above. It should be ordered: small, medium, large. As sizes are strings we cannot use the sort_values function. Here comes reindex function to the rescue:

尺寸在上表中具有隨機(jī)順序。 應(yīng)該訂購(gòu)：小，中，大。 由于大小是字符串，因此我們不能使用sort_values函數(shù)。 這里有reindex函數(shù)來(lái)解救：

df_avg.reindex(['small', 'medium', 'large'])

By

通過(guò)

3.描述類固醇 (3. Describe on steroids)

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Describe function is an essential tool when working on Exploratory Data Analysis. It shows basic summary statistics for all columns in a DataFrame.

當(dāng)進(jìn)行探索性數(shù)據(jù)分析時(shí)，描述功能是必不可少的工具。 它顯示了DataFrame中所有列的基本摘要統(tǒng)計(jì)信息。

df.price.describe()

What if we would like to calculate 10 quantiles instead of 3?

如果我們想計(jì)算10個(gè)分位數(shù)而不是3個(gè)分位數(shù)怎么辦？

df.price.describe(percentiles=np.arange(0, 1, 0.1))

Describe function takes percentiles argument. We can specify the number of percentiles with NumPy's arange function to avoid typing each percentile by hand.

描述函數(shù)采用百分位數(shù)參數(shù)。 我們可以使用NumPy的arange函數(shù)指定百分位數(shù)，以避免手動(dòng)鍵入每個(gè)百分位數(shù)。

This feature becomes really useful when combined with the group by function:

與group by函數(shù)結(jié)合使用時(shí)，此功能將非常有用：

df.groupby('size').describe(percentiles=np.arange(0, 1, 0.1))

4.使用正則表達(dá)式進(jìn)行文本搜索 (4. Text search with regex)

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Our T-shirt dataset has 3 sizes. Let’s say we would like to filter small and medium sizes. A cumbersome way of filtering is:

我們的T恤數(shù)據(jù)集有3種尺寸。 假設(shè)我們要過(guò)濾中小型尺寸。 繁瑣的過(guò)濾方式是：

df[(df['size'] == 'small') | (df['size'] == 'medium')]

This is bad because we usually combine it with other filters, which makes the expression unreadable. Is there a better way?

這很不好，因?yàn)槲覀兺ǔ⑵渑c其他過(guò)濾器結(jié)合使用，從而使表達(dá)式不可讀。 有沒(méi)有更好的辦法？

pandas string columns have an “str” accessor, which implements many functions that simplify manipulating string. One of them is “contains” function, which supports search with regular expressions.

pandas字符串列具有“ str”訪問(wèn)器，該訪問(wèn)器實(shí)現(xiàn)了許多簡(jiǎn)化操作字符串的功能。 其中之一是“包含”功能，該功能支持使用正則表達(dá)式進(jìn)行搜索。

df[df['size'].str.contains('small|medium')]

The filter with “contains” function is more readable, easier to extend and combine with other filters.

具有“包含”功能的過(guò)濾器更具可讀性，更易于擴(kuò)展并與其他過(guò)濾器組合。

5.比帶有熊貓的內(nèi)存數(shù)據(jù)集更大 (5. Bigger than memory datasets with pandas)

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pandas cannot even read bigger than the main memory datasets. It throws a MemoryError or Jupyter Kernel crashes. But to process a big dataset you don’t need Dask or Vaex. You just need some ingenuity. Sounds too good to be true?

熊貓讀取的數(shù)據(jù)甚至不能超過(guò)主內(nèi)存數(shù)據(jù)集。 它引發(fā)MemoryError或Jupyter Kernel崩潰。 但是，要處理大型數(shù)據(jù)集，您不需要Dask或Vaex。 您只需要一些獨(dú)創(chuàng)性 。 聽(tīng)起來(lái)好得令人難以置信？

In case you’ve missed my article about Dask and Vaex with bigger than main memory datasets:

如果您錯(cuò)過(guò)了我的有關(guān)Dask和Vaex的文章，而這篇文章的內(nèi)容比主內(nèi)存數(shù)據(jù)集還大：

When doing an analysis you usually don’t need all rows or all columns in the dataset.

執(zhí)行分析時(shí)，通常不需要數(shù)據(jù)集中的所有行或所有列。

In a case, you don’t need all rows, you can read the dataset in chunks and filter unnecessary rows to reduce the memory usage:

在某種情況下，您不需要所有行，您可以按塊讀取數(shù)據(jù)集并過(guò)濾不必要的行以減少內(nèi)存使用量：

iter_csv = pd.read_csv('dataset.csv', iterator=True, chunksize=1000)
df = pd.concat([chunk[chunk['field'] > constant] for chunk in iter_csv])

Reading a dataset in chunks is slower than reading it all once. I would recommend using this approach only with bigger than memory datasets.

分塊讀取數(shù)據(jù)集要比一次讀取所有數(shù)據(jù)集慢。 我建議僅對(duì)大于內(nèi)存的數(shù)據(jù)集使用此方法。

In a case, you don’t need all columns, you can specify required columns with “usecols” argument when reading a dataset:

在某種情況下，不需要所有列，可以在讀取數(shù)據(jù)集時(shí)使用“ usecols”參數(shù)指定所需的列：

df = pd.read_csvsecols=['col1', 'col2'])

The great thing about these two approaches is that you can combine them.

這兩種方法的優(yōu)點(diǎn)在于您可以將它們組合在一起。

你走之前 (Before you go)

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These are a few links that might interest you:

這些鏈接可能會(huì)讓您感興趣：

- Your First Machine Learning Model in the Cloud- AI for Healthcare- Parallels Desktop 50% off- School of Autonomous Systems- Data Science Nanodegree Program- 5 lesser-known pandas tricks- How NOT to write pandas code

翻譯自: https://towardsdatascience.com/5-essential-pandas-tricks-you-didnt-know-about-2d1a5b6f2e7

魅族mx5游戲模式小熊貓

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