What are Python pandas equivalents for R functions like str(), summary(), and head()?

Question

I'm only aware of the describe() function. Are there any other functions similar to str(), summary(), and head()?

Answer 1

I don't think there is a direct equivalent to the str() function (or glimpse() from dplyr) in Pandas that gives the same information. I think an equivalent function would have to display the following:

1. The number of rows and columns in the data frame
2. The names of all the columns
3. The data type stored in each column
4. A quick look at the first few values in each column

Building on @jjurach's answer, I wrote a helper function that works as a stand-in for the R str or glimpse function to quickly get an overview of my DataFrames. Here's the code with an example:

import pandas as pd
import random

# an example dataframe to test the helper function
example_df = pd.DataFrame({
    "var_a": [random.choice(["foo","bar"]) for i in range(20)],
    "var_b": [random.randint(0, 1) for i in range(20)],
    "var_c": [random.random() for i in range(20)]
})

# helper function for viewing pandas dataframes
def glimpse_pd(df, max_width=76):

    # find the max string lengths of the column names and dtypes for formatting
    _max_len = max([len(col) for col in df])
    _max_dtype_label_len = max([len(str(df[col].dtype)) for col in df])

    # print the dimensions of the dataframe
    print(f"{type(df)}:  {df.shape[0]} rows of {df.shape[1]} columns")

    # print the name, dtype and first few values of each column
    for _column in df:

        _col_vals = df[_column].head(max_width).to_list()
        _col_type = str(df[_column].dtype)

        output_col = f"{_column}:".ljust(_max_len+1, ' ')
        output_dtype = f" {_col_type}".ljust(_max_dtype_label_len+3, ' ')

        output_combined = f"{output_col} {output_dtype} {_col_vals}"

        # trim the output if too long
        if len(output_combined) > max_width:
            output_combined = output_combined[0:(max_width-4)] + " ..."

        print(output_combined)

Running the function returns the following output:

glimpse_pd(example_df)
<class 'pandas.core.frame.DataFrame'>:  20 rows of 3 columns
var_a:  object    ['foo', 'bar', 'foo', 'foo', 'bar', 'bar', 'foo', 'bar ...
var_b:  int64     [0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, ...
var_c:  float64   [0.7346545694885085, 0.7776711488732364, 0.49558114902 ...

Answer 2

I still prefer str() because it list some examples. A confusing aspect of info is that its behavior depends on some environment settings like pandas.options.display.max_info_columns.

I think the best alternative is to call info with some other parameters that will force a fixed behavior:

df.info(null_counts=True, verbose=True)

And for your other functions:

summary(df)     | df.describe()
head(df)        | df.head()
dim(df)         | df.shape

Answer 3

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.

R               | Pandas
-------------------------------
summary(df)     | df.describe()
head(df)        | df.head()
dim(df)         | df.shape
slice(df, 1:10) | df.iloc[:9]

Answer 4

This provides output similar to R's str(). It presents unique values instead of initial values.

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)

Answer 5

• summary() ~ describe()
• head() ~ head()

I'm not sure about the str() equivalent.

Answer 6

For a Python equivalent to the str() function in R, I use the method dtypes. This will provide the data types for each column.

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

Answer 7

In pandas the info() method creates a very similar output like R's 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

Answer 8

I don't know much about R, but here are some leads:

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...

summary => describe. 

See the parameters to customize the results.

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]