Peeking at data with head, tail, and describe

Intermediate Python for Finance

Kennedy Behrman

Data Engineer, Author, Founder

Understanding your data

Data is loaded correctly
Understand the data's shape

First look at data

aapl

First look at data

aapl


Date
03/27/2020
03/26/2020
03/25/2020
03/24/2020

First look at data

aapl

	Price
Date
03/27/2020	247.74
03/26/2020	258.44
03/25/2020	245.52
03/24/2020	246.88

First look at data

aapl

	Price	Volume
Date
03/27/2020	247.74	51054150
03/26/2020	258.44	63140170
03/25/2020	245.52	75900510
03/24/2020	246.88	71882770

First look at data

aapl

	Price	Volume	Trend
Date
03/27/2020	247.74	51054150	Down
03/26/2020	258.44	63140170	Up
03/25/2020	245.52	75900510	Down
03/24/2020	246.88	71882770	Up

Head

aapl.head()

             Price    Volumne    Trend
Date
03/27/2020   247.74   51054150   Down
03/26/2020   258.44   63140170   Up
03/25/2020   245.52   75900510   Down
03/24/2020   246.88   71882770   Up
03/23/2020   224.37   84188210   Down

Head

aapl.head()

Head

aapl.head(3)

```out
             Price    Volumne    Trend
Date
03/27/2020   247.74   51054150   Down
03/26/2020   258.44   63140170   Up
03/25/2020   245.52   75900510   Down

Tail

aapl.tail()

             Price    Volumne    Trend
Date
03/05/2020   292.92   46893220   Down
03/04/2020   302.74   54794570   Up
03/03/2020   289.32   79868850   Down
03/02/2020   298.81   85349340   Up
02/28/2020   273.36   106721200  Down

Describe

aapl.describe()

       Price        Volume
count  21.000000    2.100000e+01
mean   263.715714   7.551468e+07
std    23.360598    1.669757e+07
min    224.370000   4.689322e+07
25%    246.670000   6.409497e+07
50%    258.440000   7.505841e+07
75%    285.340000   8.418821e+07
max    302.740000   1.067212e+08

Include

aapl.describe(include='object')

        Trend
count   21
unique  2
top     Down
freq    14

Include

aapl.describe(include='all')

         Price       Volumne        Trend
count    21.000000   2.100000e+01   21
unique   NaN         NaN            2
top      NaN         NaN            Down
freq     NaN         NaN            14
mean     263.715714  7.551468e+07   NaN
std      23.360598   1.669757e+07   NaN
min      224.370000  4.689322e+07   NaN
25%      246.670000  6.409497e+07   NaN

aapl.describe(include=['float', 'object'])

       Price        Trend
count  21.000000    21
unique NaN          2
top    NaN          Down
freq   NaN          14
mean   263.715714   NaN
std    23.360598    NaN
min    224.370000   NaN
25%    246.670000   NaN
50%    258.440000   NaN
75%    285.340000   NaN
max    302.740000   NaN

Percentiles

aapl.describe(percentiles=[.1, .5, .9])

      Price       Volumne
count 21.000000   2.100000e+01
mean  263.715714  7.551468e+07
std   23.360598   1.669757e+07
min   224.370000  4.689322e+07
10%   242.210000  5.479457e+07
50%   258.440000  7.505841e+07
90%   292.920000  1.004233e+08
max   302.740000  1.067212e+08

Exclude

aapl.describe(exclude='float')

       Volumne        Trend
count  2.100000e+01   21
unique NaN            2
top    NaN            Down
freq   NaN            14
mean   7.551468e+07   NaN
std    1.669757e+07   NaN
min    4.689322e+07   NaN
25%    6.409497e+07   NaN

Let's practice!

Intermediate Python for Finance