Converting to Markdown We’ve added to_markdown() for creating a markdown table (GH11052) In [1]: df = pd.DataFrame({"A": [1, 2, 3], "B": [1, 2, 3]}, index=['a', 'a', 'b']) In [2]: print(df.to_markdown()) | 3 | 1.3 Experimental new features 1.3.1 Experimental NA scalar to denote missing values A new pd.NA value (singleton) is introduced to represent scalar missing values. Up to now, pandas used several or None for object-dtype data and pd.NaT for datetime-like data. The goal of pd.NA is to provide a “missing” indicator that can be used consistently across data types. pd.NA is currently used by the nullable

soft and hard, installed), make sure you have pytest >= 5.0.1 and Hypothesis >= 3.58, then run: >>> pd.test() running: pytest --skip-slow --skip-network C:\Users\TP\Anaconda3\envs\py36\lib\site- ˓→packages\pandas pandas In [1]: import pandas as pd To load the pandas package and start working with it, import the package. The community agreed alias for pandas is pd, so loading pandas as pd is assumed standard practice passengers, I know the name (characters), age (integers) and sex (male/female) data. In [2]: df = pd.DataFrame({ ...: "Name": ["Braund, Mr. Owen Harris", ...: "Allen, Mr. William Henry", ...: "Bonnell

soft and hard, installed), make sure you have pytest >= 5.0.1 and Hypothesis >= 3.58, then run: >>> pd.test() running: pytest --skip-slow --skip-network C:\Users\TP\Anaconda3\envs\py36\lib\site- ˓→packages\pandas pandas In [1]: import pandas as pd To load the pandas package and start working with it, import the package. The community agreed alias for pandas is pd, so loading pandas as pd is assumed standard practice passengers, I know the name (characters), age (integers) and sex (male/female) data. In [2]: df = pd.DataFrame({ ...: "Name": ["Braund, Mr. Owen Harris", ...: "Allen, Mr. William Henry", ...: "Bonnell

soft and hard, installed), make sure you have pytest >= 6.0 and Hypothesis >= 6.13.0, then run: >>> pd.test() running: pytest --skip-slow --skip-network --skip-db /home/user/anaconda3/lib/python3.9/ ˓ pandas In [1]: import pandas as pd To load the pandas package and start working with it, import the package. The community agreed alias for pandas is pd, so loading pandas as pd is assumed standard practice passengers, I know the name (characters), age (integers) and sex (male/female) data. In [2]: df = pd.DataFrame( ...: { ...: "Name": [ ...: "Braund, Mr. Owen Harris", ...: "Allen, Mr. William Henry"

applying multiple aggregation functions to specific columns (GH18366, GH26512). In [1]: animals = pd.DataFrame({'kind': ['cat', 'dog', 'cat', 'dog'], ...: 'height': [9.1, 6.0, 9.5, 34.0], ...: 'weight': groupby("kind").agg( ...: min_height=pd.NamedAgg(column='height', aggfunc='min'), ...: max_height=pd.NamedAgg(column='height', aggfunc='max'), ...: average_weight=pd.NamedAgg(column='weight', aggfunc=np easier to understand the structure of the MultiIndex. (GH13480): The repr now looks like this: In [8]: pd.MultiIndex.from_product([['a', 'abc'], range(500)]) Out[8]: MultiIndex([( 'a', 0), ( 'a', 1), ( 'a'

applying multiple aggregation functions to specific columns (GH18366, GH26512). In [1]: animals = pd.DataFrame({'kind': ['cat', 'dog', 'cat', 'dog'], ...: 'height': [9.1, 6.0, 9.5, 34.0], ...: 'weight': groupby("kind").agg( ...: min_height=pd.NamedAgg(column='height', aggfunc='min'), ...: max_height=pd.NamedAgg(column='height', aggfunc='max'), ...: average_weight=pd.NamedAgg(column='weight', aggfunc=np easier to understand the structure of the MultiIndex. (GH13480): The repr now looks like this: In [8]: pd.MultiIndex.from_product([['a', 'abc'], range(500)]) Out[8]: MultiIndex([( 'a', 0), ( 'a', 1), ( 'a'

soft and hard, installed), make sure you have pytest >= 6.0 and Hypothesis >= 3.58, then run: >>> pd.test() running: pytest --skip-slow --skip-network C:\Users\TP\Anaconda3\envs\py36\lib\site- ˓→packages\pandas pandas In [1]: import pandas as pd To load the pandas package and start working with it, import the package. The community agreed alias for pandas is pd, so loading pandas as pd is assumed standard practice passengers, I know the name (characters), age (integers) and sex (male/female) data. In [2]: df = pd.DataFrame( ...: { ...: "Name": [ ...: "Braund, Mr. Owen Harris", ...: "Allen, Mr. William Henry"

soft and hard, installed), make sure you have pytest >= 6.0 and Hypothesis >= 3.58, then run: >>> pd.test() running: pytest --skip-slow --skip-network C:\Users\TP\Anaconda3\envs\py36\lib\site- ˓→packages\pandas pandas In [1]: import pandas as pd To load the pandas package and start working with it, import the package. The community agreed alias for pandas is pd, so loading pandas as pd is assumed standard practice passengers, I know the name (characters), age (integers) and sex (male/female) data. In [2]: df = pd.DataFrame( ...: { ...: "Name": [ ...: "Braund, Mr. Owen Harris", ...: "Allen, Mr. William Henry"

soft and hard, installed), make sure you have pytest >= 6.0 and Hypothesis >= 3.58, then run: >>> pd.test() running: pytest --skip-slow --skip-network C:\Users\TP\Anaconda3\envs\py36\lib\site- ˓→packages\pandas pandas In [1]: import pandas as pd To load the pandas package and start working with it, import the package. The community agreed alias for pandas is pd, so loading pandas as pd is assumed standard practice passengers, I know the name (characters), age (integers) and sex (male/female) data. In [2]: df = pd.DataFrame( ...: { ...: "Name": [ ...: "Braund, Mr. Owen Harris", ...: "Allen, Mr. William Henry"

soft and hard, installed), make sure you have pytest >= 5.0.1 and Hypothesis >= 3.58, then run: >>> pd.test() running: pytest --skip-slow --skip-network C:\Users\TP\Anaconda3\envs\py36\lib\site- ˓→packages\pandas import pandas as pd Object creation See the Data Structure Intro section. Creating a Series by passing a list of values, letting pandas create a default integer index: In [3]: s = pd.Series([1, 3, 5 Creating a DataFrame by passing a NumPy array, with a datetime index and labeled columns: In [5]: dates = pd.date_range('20130101', periods=6) In [6]: dates Out[6]: DatetimeIndex(['2013-01-01', '2013-01-02'