什么是 Python pandas 中的 str()、summary() 和 head() 等 R 函数的等价物？
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What are Python pandas equivalents for R functions like str(), summary(), and head()?
提问by megashigger
I'm only aware of the describe()function. Are there any other functions similar to str(), summary(), and head()?
我只知道这个describe()功能。是否还有其他的功能类似str()，summary()和head()？
采纳答案by omer sagy
summary()~describe()head()~head()
summary()~describe()head()~head()
I'm not sure about the str()equivalent.
我不确定str()等价物。
回答by Wakaru44
I don't know much about R, but here are some leads:
我对 R 了解不多，但这里有一些线索：
str =>
difficult one... for functions you can use dir(), dir() on datasets will give you all the methods, so maybe that's not what you want...
困难的一个......对于你可以在数据集上使用 dir() 的函数，dir() 会给你所有的方法，所以也许这不是你想要的......
summary => describe.
See the parameters to customize the results.
查看参数以自定义结果。
head => your can use head(), or use slices.
head as you already do. To get the first 10 rows of a dataset called ds ds[:10]same for tail ds[:-10]
像你已经做的那样。获取名为 ds ds[:10]same for tail的数据集的前 10 行ds[:-10]
回答by jjurach
This provides output similar to R's str(). It presents unique values instead of initial values.
这提供了类似于 R 的输出str()。它呈现唯一值而不是初始值。
def rstr(df): return df.shape, df.apply(lambda x: [x.unique()])
print(rstr(iris))
((150, 5), sepal_length [[5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.4, 4.8, 4.3,...
sepal_width [[3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 2.9, 3.7,...
petal_length [[1.4, 1.3, 1.5, 1.7, 1.6, 1.1, 1.2, 1.0, 1.9,...
petal_width [[0.2, 0.4, 0.3, 0.1, 0.5, 0.6, 1.4, 1.5, 1.3,...
class [[Iris-setosa, Iris-versicolor, Iris-virginica]]
dtype: object)
回答by Martin Thoma
Pandas offers an extensive Comparison with R / R libraries. The most obvious difference is that R prefers functional programming while Pandas is object orientated, with the data frame as the key object. Another difference between R and Python is that Python starts arrays at 0, but R at 1.
Pandas 提供了与 R/R 库的广泛比较。最明显的区别是 R 更喜欢函数式编程，而 Pandas 是面向对象的，以数据框为关键对象。R 和 Python 的另一个区别是 Python 从 0 开始数组，而 R 从 1 开始。
R | Pandas
-------------------------------
summary(df) | df.describe()
head(df) | df.head()
dim(df) | df.shape
slice(df, 1:10) | df.iloc[:9]
回答by fubar2021
For a Python equivalent to the str()function in R, I use the method dtypes. This will provide the data types for each column.
对于str()与 R 中的函数等效的 Python ，我使用方法dtypes. 这将为每一列提供数据类型。
In [22]: df2.dtypes
Out[22]:
Survived int64
Pclass int64
Sex object
Age float64
SibSp int64
Parch int64
Ticket object
Fare float64
Cabin object
Embarked object
dtype: object
回答by reedcourty
In pandas the info()method creates a very similar output like R's str():
在 Pandas 中，该info()方法创建了一个与 R 非常相似的输出str()：
> str(train)
'data.frame': 891 obs. of 13 variables:
$ PassengerId: int 1 2 3 4 5 6 7 8 9 10 ...
$ Survived : int 0 1 1 1 0 0 0 0 1 1 ...
$ Pclass : int 3 1 3 1 3 3 1 3 3 2 ...
$ Name : Factor w/ 891 levels "Abbing, Mr. Anthony",..: 109 191 358 277 16 559 520 629 417 581 ...
$ Sex : Factor w/ 2 levels "female","male": 2 1 1 1 2 2 2 2 1 1 ...
$ Age : num 22 38 26 35 35 NA 54 2 27 14 ...
$ SibSp : int 1 1 0 1 0 0 0 3 0 1 ...
$ Parch : int 0 0 0 0 0 0 0 1 2 0 ...
$ Ticket : Factor w/ 681 levels "110152","110413",..: 524 597 670 50 473 276 86 396 345 133 ...
$ Fare : num 7.25 71.28 7.92 53.1 8.05 ...
$ Cabin : Factor w/ 148 levels "","A10","A14",..: 1 83 1 57 1 1 131 1 1 1 ...
$ Embarked : Factor w/ 4 levels "","C","Q","S": 4 2 4 4 4 3 4 4 4 2 ...
$ Child : num 0 0 0 0 0 NA 0 1 0 1 ...
train.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
PassengerId 891 non-null int64
Survived 891 non-null int64
Pclass 891 non-null int64
Name 891 non-null object
Sex 891 non-null object
Age 714 non-null float64
SibSp 891 non-null int64
Parch 891 non-null int64
Ticket 891 non-null object
Fare 891 non-null float64
Cabin 204 non-null object
Embarked 889 non-null object
dtypes: float64(2), int64(5), object(5)
memory usage: 83.6+ KB
回答by neves
I still prefer str()because it list some examples. A confusing aspect of infois that its behavior depends on some environment settings like pandas.options.display.max_info_columns.
我仍然更喜欢，str()因为它列出了一些例子。的一个令人困惑的方面info是它的行为取决于某些环境设置，例如pandas.options.display.max_info_columns.
I think the best alternative is to call infowith some other parameters that will force a fixed behavior:
我认为最好的选择是info使用其他一些强制固定行为的参数进行调用：
df.info(null_counts=True, verbose=True)
And for your other functions:
对于您的其他功能：
summary(df) | df.describe()
head(df) | df.head()
dim(df) | df.shape
