cases, iterating manually over the rows is not needed and can be avoided with one of the following approaches: 3.3. Essential basic functionality 79 pandas: powerful Python data analysis toolkit, Release substring from a string based on its text by using regular expressions. There are much more powerful approaches, but this just shows a simple approach. In [30]: firstlast = pd.DataFrame({'String': ['John Smith' substring from a string based on its text by using regular expressions. There are much more powerful approaches, but this just shows a simple approach. In [31]: firstlast = pd.DataFrame({'string': ['John Smith'

cases, iterating manually over the rows is not needed and can be avoided with one of the following approaches: • Look for a vectorized solution: many operations can be performed using built-in methods or substring from a string based on its text by using regular expressions. There are much more powerful approaches, but this just shows a simple approach. In [30]: firstlast = pd.DataFrame({'String': ['John Smith' substring from a string based on its text by using regular expressions. There are much more powerful approaches, but this just shows a simple approach. 210 Chapter 2. Getting started pandas: powerful Python

[68]: df.dtypes Out[68]: A Sparse[int64, 0] Length: 1, dtype: object The memory usage of the two approaches is identical. See Migrating for more (GH19239). 1.3.2 msgpack format The msgpack format is deprecated cases, iterating manually over the rows is not needed and can be avoided with one of the following approaches: • Look for a vectorized solution: many operations can be performed using built-in methods or substring from a string based on its text by using regular expressions. There are much more powerful approaches, but this just shows a simple approach. In [30]: firstlast = pd.DataFrame({'String': ['John Smith'

[68]: df.dtypes Out[68]: A Sparse[int64, 0] Length: 1, dtype: object The memory usage of the two approaches is identical. See Migrating for more (GH19239). 1.3.2 msgpack format The msgpack format is deprecated cases, iterating manually over the rows is not needed and can be avoided with one of the following approaches: • Look for a vectorized solution: many operations can be performed using built-in methods or substring from a string based on its text by using regular expressions. There are much more powerful approaches, but this just shows a simple approach. In [30]: firstlast = pd.DataFrame({'String': ['John Smith'

to split the strings by spaces, then reference the word by index. Note there are more powerful approaches should you need them. In [36]: firstlast = pd.DataFrame({"String": ["John Smith", "Jane Cook"]}) to split the strings by spaces, then reference the word by index. Note there are more powerful approaches should you need them. In [1]: firstlast = pd.DataFrame({"String": ["John Smith", "Jane Cook"]}) to split the strings by spaces, then reference the word by index. Note there are more powerful approaches should you need them. In [36]: firstlast = pd.DataFrame({"String": ["John Smith", "Jane Cook"]})

to split the strings by spaces, then reference the word by index. Note there are more powerful approaches should you need them. In [36]: firstlast = pd.DataFrame({"String": ["John Smith", "Jane Cook"]}) to split the strings by spaces, then reference the word by index. Note there are more powerful approaches should you need them. In [1]: firstlast = pd.DataFrame({"String": ["John Smith", "Jane Cook"]}) to split the strings by spaces, then reference the word by index. Note there are more powerful approaches should you need them. In [36]: firstlast = pd.DataFrame({"String": ["John Smith", "Jane Cook"]})

to split the strings by spaces, then reference the word by index. Note there are more powerful approaches should you need them. In [36]: firstlast = pd.DataFrame({"String": ["John Smith", "Jane Cook"]}) to split the strings by spaces, then reference the word by index. Note there are more powerful approaches should you need them. In [1]: firstlast = pd.DataFrame({"String": ["John Smith", "Jane Cook"]}) to split the strings by spaces, then reference the word by index. Note there are more powerful approaches should you need them. In [36]: firstlast = pd.DataFrame({"String": ["John Smith", "Jane Cook"]})

cases, iterating manually over the rows is not needed and can be avoided with one of the following approaches: • Look for a vectorized solution: many operations can be performed using built-in methods or substring from a string based on its text by using regular expressions. There are much more powerful approaches, but this just shows a simple approach. In [30]: firstlast = pd.DataFrame({'String': ['John Smith' substring from a string based on its text by using regular expressions. There are much more powerful approaches, but this just shows a simple approach. In [31]: firstlast = pd.DataFrame({'string': ['John Smith'

cases, iterating manually over the rows is not needed and can be avoided with one of the following approaches: • Look for a vectorized solution: many operations can be performed using built-in methods or substring from a string based on its text by using regular expressions. There are much more powerful approaches, but this just shows a simple approach. In [30]: firstlast = pd.DataFrame({'String': ['John Smith' substring from a string based on its text by using regular expressions. There are much more powerful approaches, but this just shows a simple approach. In [31]: firstlast = pd.DataFrame({'string': ['John Smith'

substring from a string based on its text by using regular expressions. There are much more powerful approaches, but this just shows a simple approach. In [30]: firstlast = pd.DataFrame({'String': ['John Smith' substring from a string based on its text by using regular expressions. There are much more powerful approaches, but this just shows a simple approach. 1.4. Tutorials 103 pandas: powerful Python data analysis subsections introduces a topic (such as “working with missing data”), and discusses how pandas approaches the problem, with many examples throughout. Users brand-new to pandas should start with 10min