1.4. About Workflow

1.4.1. SetUp

>>> import pandas as pd
>>> import numpy as np

1.4.2. Working with Excel file

>>> 
... df = pd.read_excel(
...     io='filename.xls',
...     sheet_name=['Sheet 1'],
...     skiprows=1,
...     skip_blank_lines=True,
...     parse_dates=['from', 'to'],
... )
...
... # Rename Columns to match database columns
... df.rename(columns={
...     'from': 'date_start',
...     'to': 'date_end',
... }, inplace=True)
...
... # Drop all records where "Name" is empty (NaN)
... df.dropna(subset=['name'], how='all', inplace=True)
...
... # Add column ``blacklist`` with data
... df['blacklist'] = [True, False, True, False]
...
... # Change NaN to 0
... df.fillna(0, inplace=True)
...
... # Select columns
... columns = ['name', 'date_start', 'date_end', 'blacklist']
...
... # Print results
... print( df[columns] )

1.4.3. Working with dirty CSV

>>> DATA = 'https://python3.info/_static/iris-dirty.csv'
>>> COLUMNS = ['sepal_length', 'sepal_width',
...            'petal_length', 'petal_width', 'species']
>>>
>>> nrows, ncols, *class_labels = pd.read_csv(DATA, nrows=0).columns
>>> label_encoder = dict(enumerate(class_labels))
>>>
>>> df = pd.read_csv(DATA, skiprows=1, names=COLUMNS)
>>> df['species'].replace(label_encoder, inplace=True)
>>> plot = df.plot(kind='density')  

1.4.4. Working with CSV

>>> DATA = 'https://python3.info/_static/iris-clean.csv'

Read data from source:

>>> df = pd.read_csv(DATA)

Rename columns:

>>> df.columns = ['sepal_length', 'sepal_width',
...               'petal_length', 'petal_width', 'species']

Get first n records:

>>> df.head(n=5)
   sepal_length  sepal_width  petal_length  petal_width     species
0           5.4          3.9           1.3          0.4      setosa
1           5.9          3.0           5.1          1.8   virginica
2           6.0          3.4           4.5          1.6  versicolor
3           7.3          2.9           6.3          1.8   virginica
4           5.6          2.5           3.9          1.1  versicolor

Get last n records:

>>> df.tail(n=3)
     sepal_length  sepal_width  petal_length  petal_width    species
148           4.9          2.5           4.5          1.7  virginica
149           6.3          2.8           5.1          1.5  virginica
150           6.8          3.2           5.9          2.3  virginica

Shuffle columns and reset indexes (drop column with old index):

>>> np.random.seed(0)
>>> df.sample(n=10).reset_index(drop=True)
   sepal_length  sepal_width  petal_length  petal_width     species
0           6.7          3.3           5.7          2.1   virginica
1           6.5          2.8           4.6          1.5  versicolor
2           6.3          2.3           4.4          1.3  versicolor
3           6.8          2.8           4.8          1.4  versicolor
4           5.7          2.9           4.2          1.3  versicolor
5           6.3          3.4           5.6          2.4   virginica
6           5.5          2.4           3.8          1.1  versicolor
7           6.9          3.1           5.4          2.1   virginica
8           6.3          2.5           4.9          1.5  versicolor
9           4.9          3.1           1.5          0.2      setosa

Calculate descriptive statistics:

>>> df.describe()
       sepal_length  sepal_width  petal_length  petal_width
count    151.000000   151.000000    151.000000   151.000000
mean       5.840397     3.062914      3.741722     1.194040
std        0.826089     0.439790      1.770738     0.762472
min        4.300000     2.000000      1.000000     0.100000
25%        5.100000     2.800000      1.550000     0.300000
50%        5.800000     3.000000      4.300000     1.300000
75%        6.400000     3.350000      5.100000     1.800000
max        7.900000     4.400000      6.900000     2.500000

Table 1.3. Descriptive statistics

Function	Description
count	Number of non-null observations
sum	Sum of values
mean	Mean of values
mad	Mean absolute deviation
median	Arithmetic median of values
min	Minimum
max	Maximum
mode	Mode
abs	Absolute Value
prod	Product of values
std	Unbiased standard deviation
var	Unbiased variance
sem	Unbiased standard error of the mean
skew	Unbiased skewness (3rd moment)
kurt	Unbiased kurtosis (4th moment)
quantile	Sample quantile (value at %)
cumsum	Cumulative sum
cumprod	Cumulative product
cummax	Cumulative maximum
cummin	Cumulative minimum

1.4.5. Hist Plot

>>> import matplotlib.pyplot as plt
>>> import pandas as pd
>>>
>>>
>>> DATA = 'https://python3.info/_static/iris-clean.csv'
>>>
>>> df = pd.read_csv(DATA)
>>> plot = df.hist()
>>> plt.show()  

Figure 1.8. Visualization using hist

1.4.6. Density Plot

>>> import matplotlib.pyplot as plt
>>> import pandas as pd
>>>
>>>
>>> DATA = 'https://python3.info/_static/iris-clean.csv'
>>>
>>> df = pd.read_csv(DATA)
>>> plot = df.plot(kind='density', subplots=True, layout=(2,2), sharex=False)
>>> plt.show()  

Figure 1.9. Visualization using density

1.4.7. Box Plot

>>> import matplotlib.pyplot as plt
>>> import pandas as pd
>>>
>>>
>>> DATA = 'https://python3.info/_static/iris-clean.csv'
>>>
>>> df = pd.read_csv(DATA)
>>> plot = df.plot(kind='box', subplots=True, layout=(2,2), sharex=False, sharey=False)
>>> plt.show()  

Figure 1.10. Visualization using density

1.4.8. Scatter matrix

>>> import matplotlib.pyplot as plt
>>> import pandas as pd
>>> from pandas.plotting import scatter_matrix
>>>
>>>
>>> DATA = 'https://python3.info/_static/iris-clean.csv'
>>>
>>> df = pd.read_csv(DATA)
>>> plot = scatter_matrix(df)
>>> plt.show()  

Figure 1.11. Visualization using density