Lab 5: pandas

Objective

To learn the basic of the pandas Python library.

Data structures

In pandas, there are two types of data structures:

Structure	Description
Series	1D labeled homogeneously-typed array
DataFrame	General 2D labeled, size-mutable tabular structure with potentially heterogeneously-typed column

Instruction

For all sections in this lab other than the last section, use the IPython console (located normally at the right bottom corner) to run the codes.

Imports

To import the pandas library,
```
import pandas as pd
```
NumPy is a dependency of pandas and also a powerful Python library for scientific data processing. We may need to use NumPy from time to time. To import Numpy,
```
import numpy as np
```
It's common practice to import pandas as pd and numpy as np. You would see this a lot if you tried to search for tutorials or solutions online. However, it's just a convention, it is fine to use other names.

Series

Creation

from list,

s1 = pd.Series([1, 3, 5, np.nan, 6, 8])
s2 = pd.Series([1, 3, 5, np.nan, 6, 8], index=[1, 2, 3, 4, 5, 'f'])

What is the difference between s1 and s2?

from dict,

d = {'a': 1, 'b': 2, 'c': 3}
s3 = pd.Series(d)

from scalar value,

s4 = pd.Series(5, index=['a', 'b', 'c', 'd', 'e'])

Indexing of Series

try the following code to understand the getting and setting of a series with default indexing.

s = pd.Series(np.random.randn(5))
s[0]
s[0] = 1.5
s

if the labels for the indices are specified,

s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])
s['a']
s[0]
s['b'] = 1.8
s
s[2] = 2
s

DataFrame

Creation

from NumPy array

df = pd.DataFrame(np.random.randn(6,4))

Indices and column names can be provided at creation.

df = pd.DataFrame(np.random.randn(6,4), index=list('abcdef'), columns=list('ABCD'))

from a dict

run the following code and understand the functions used.

df2 = pd.DataFrame({
  'A': 1,
  'B': pd.Timestamp('20190930'),
  'C': pd.date_range('20190930', periods=4),
  'D': pd.Series(1, index=list(range(4)), dtype='float32'),
  'E': np.array([3]*4, dtype='int32'),
  'F': pd.Categorical(['test', 'train', 'test', 'train']),
  'G': 'foo'
})

dtypes of a DataFrame can be viewed using df2.dtypes. In IPython, tab completion is enabled for column names and public attributes.

Data display
- What do df.head(0) and df.tail() do? What happens if I use df.head(3) and df.tail(2)?
- df.index displays the indices of a data frame.
- df.columns displays the columns of a data frame.
- df.describe() shows a quick statistical summary of each column of the data.
Direct indexing
- to get a column,
```
df['A']
df.A
```
df[0] would not work.
- to select multiple columns,
```
df[['A', 'B']]
```
- to get a slice of rows
```
df[0:4]
df['a':'d']
```
Is the indexing inclusive or exclusive?

Selection by label With the following lines, identify how the function .loc[...] works

df.loc['a']
df.loc['a':'c']
df.loc[['a', 'c']]
df.loc[:, 'A']
df.loc[:, ['A', 'B']]
df.loc[:, 'A':'C']
df.loc['a':'c', ['A', 'B']]
df.loc[['a', 'c'], ['A', 'B']]
df.loc[['a', 'c'], 'A':'C']
df.loc['a':'c', 'A':'C']
df.loc['a', 'A']
df.loc['a', 'A':'C']

df.at['a','A'] is equivalent to df.loc['a','A'] (only to get a scalar value)

The object returned by a .loc is either a series (1-D), data frame (2-D), or scalar (single value).

Selection by position .iloc and .iat work similarly as .loc and .at. The only difference is that, instead of the label of the row/column, we will use the position of the row/column.

Find the equivalent usage of .iloc that provides the same outputs as the previous lines for .loc.

Boolean indexing Investigate the differences in the outputs of the following lines:

df[df.A > 0]
df[df > 0]

Filtering of a column can be done with .isin.

df2 = df.copy()
df2['E'] = ['one', 'one', 'two', 'three', 'four', 'three']
df2[df2['E'].isini(['two', 'four'])]

Data manipulation

pandas library provides a lot of functions to manipulate data.

Go to UCI datasets to download iris.data and iris.names from the Data Folder.

Load iris.data as a data frame (Hint: iris.data is a CSV file)
Update the column names based on iris.names.
Calculate the mean, min, max, and standard deviation of each column.
Create a new column called class value using the following code:
```
df['class value'] = pd.factorize(df['class'])[0]
```
Investigate the output of pd.factorize.
Group the data according to the class. (Hint: .groupby)
Identify the function to extract each group using the name of the class.
Calculate the mean, min, max, and standard deviation of each column in each group.
Produce a scatter plot for any two columns using matplotlib library.
Identify the methods (at least 2) to loop through a data frame row by row.