Chapter 1

df.head()
df.tail()
df.info()

prints first five entries
prints last five entries
prints the info of all columns

NumPy and pandas working together

import numpy as np

# Create array of DataFrame values: np_vals
np_vals = df.values

Building DataFrames from scratch

list_keys = ['Country', 'Total']
list_values = [['United States', 'Soviet Union', 'United Kingdom'], [1118, 473, 273]]

zipped = list(zip(list_keys , list_values))
print(zipped)
data = dict(zipped)
df = pd.DataFrame(data)
print(df)

[ ( 'Country', [ 'United States', 'Soviet Union', 'United Kingdom' ] ), ( 'Total', [1118, 473, 273] ) ]

	Country	Total
0	United States	1118
1	Soviet Union	473
2	United Kingdom	273

Building DataFrames with broadcasting

state = "PA"
cities=['Manheim','Preston park','Biglerville']

data = {'state':state, 'city':cities}

df = pd.DataFrame(data)
print(df)

	state	city
0	PA	Manheim
1	PA	Preston park
2	PA	Biglerville

Importing & exporting data

Reading a flat file

df1 = pd.read_csv(data_file)
print(df1)

	Year	Total Population
0	1960	3.034971e+09
1	1970	3.684823e+09
2	1980	4.436590e+09
3	1990	5.282716e+09
4	2000	6.115974e+09
5	2010	6.924283e+09

new_labels = ['year', 'population']

df2 = pd.read_csv(data_file, header=0, names=new_labels)
print(df2)

	year	population
0	1960	3.034971e+09
1	1970	3.684823e+09
2	1980	4.436590e+09
3	1990	5.282716e+09
4	2000	6.115974e+09
5	2010	6.924283e+09

Delimiters, headers, and extensions

df1 = pd.read_csv(file_messy)
print(df1.head())

df2 = pd.read_csv(file_messy, delimiter=' ', header=3, comment='#')
print(df2.head())

df2.to_csv(file_clean, index=False)
df2.to_excel('file_clean.xlsx', index=False)

The following stock data was collect on 2016-AUG-25 from an unknown source
These kind of comments are not very useful are they?

Probably should just throw this line away too but not the next since those are column labels
name Jan Feb Mar Apr May Jun Jul Aug SepOct No...NaN

# So that line you just read has all the column...NaN

IBM 156.08 160.01 159.81 165.22 172.25 167.15 1...NaN

	name	Jan	Feb	...	Nov	Dec
0	IBM	156.08	160.01	...	137.21	137.96
1	MSFT	45.51	43.08	...	53.88	55.40
2	GOOGLE	512.42	537.99	...	735.39	755.35
3	APPLE	110.64	125.43	...	118.16	111.73
[4 rows x 13 columns]

Plotting series using pandas

df.plot(color='red')

plt.title('Temperature in Austin')
plt.xlabel('Hours since midnight August 1, 2010')
plt.ylabel('Temperature (degrees F)')

print(df.head(3))
plt.show()

	Temperature (deg F)
0	79.0
1	77.4
2	76.4

Plotting DataFrames

print(df.head())

	Temperature (deg F)	Dew Point (deg F)	Pressure (atm)
0	79.0	70.8	1.0
1	77.4	71.2	1.0
2	76.4	71.3	1.0
3	75.7	71.4	1.0
4	75.1	71.4	1.0

df.plot()
plt.show()

df.plot(subplots=True)
plt.show()

column_list1 = ['Dew Point (deg F)']
df[column_list1].plot()
plt.show()

column_list2 = ['Temperature (deg F)','Dew Point (deg F)']
df[column_list2].plot()
plt.show()