Introduction to Pandas

PANDAS
A POWERFUL DATA MANIPULATION TOOL
/ /EmmaJasonAMyers @jasonamyers

WHAT'S SO SPECIAL ABOUT PANDAS?
1. Tabular/Matrix
2. DataFlexibility
3. DataManipulation
4. Time Series

INSTALLATIONpipinstallpandas
pipinstallpandasaspd

PANDAS DATA STRUCTURES
Series -basicallyan ordered dictthatcan be named
Dataframe -Alabeled two dimensionaldatatype

SERIES
importpandasaspd
cookies=pd.Series(
[
'ChocolateChip,'
'PeanutButter,'
'GingerMolasses,'
'OatmealRaisin,'
'Sugar',
'Oreo',
]
)

WHAT DOES IT LOOK LIKE?
0 ChocolateChip
1 PeanutButter
2 GingerMolasses
3 OatmealRaisin
4 Sugar
5 Oreo
dtype:object

PROPERTIES
>>>cookies.values
array(['ChocolateChip','PeanutButter','GingerMolasses',
'OatmealRaisin','Sugar','Oreo'],dtype=object)
>>>cookies.index
Int64Index([0,1,2,3,4,5],dtype='int64')

SPECIFYING THE INDEX
cookies=pd.Series([12,10,8,6,4,2],index=['ChocolateChip',
'PeanutButter',
'GingerMolasses',
'OatmealRaisin',
'Sugar',
'PowderSugar'
])

INDEXED SERIES
ChocolateChip 12
PeanutButter 10
GingerMolasses 8
OatmealRaisin 6
Sugar 4
PowderSugar 2
dtype:int64

NAMING THE VALUES AND INDEXES
>>>cookies.name='counts'
>>>cookies.index.name='type'
type
ChocolateChip 12
PeanutButter 10
GingerMolasses 8
OatmealRaisin 6
Sugar 4
PowderSugar 2
Name:counts,dtype:int64

ACCESSING ELEMENTS
>>>cookies[[name.endswith('Sugar')fornameincookies.index]]
Sugar 4
PowderSugar 2
dtype:int64
>>>cookies[cookies>10]
ChocolateChip 12
Name:counts,dtype:int64

DATAFRAMES
df=pd.DataFrame({
'count':[12,10,8,6,2,2,2],
'type':['ChocolateChip','PeanutButter','GingerMolasses','OatmealRaisin','Sug
'owner':['Jason','Jason','Jason','Jason','Jason','Jason','Marvin']
})

count owner type
0 12 Jason ChocolateChip
1 10 Jason PeanutButter
2 8 Jason GingerMolasses
3 6 Jason OatmealRaisin
4 2 Jason Sugar
5 2 Jason PowderSugar
6 2 Marvin Sugar

ACCESSING COLUMNS
>>>df['type']
0 ChocolateChip
1 PeanutButter
2 GingerMolasses
3 OatmealRaisin
4 Sugar
5 PowderSugar
6 Sugar
Name:type,dtype:object

ACCESSING ROWS
>>>df.loc[2]
count 8
owner Jason
type GingerMolasses
Name:2,dtype:object

SLICING ROWS
>>>df.loc[2:5]
count owner type
2 8 Jason GingerMolasses
3 6 Jason OatmealRaisin
4 2 Jason Sugar
5 2 Jason PowderSugar

PIVOTING
>>>df.loc[3:4].T
3 4
count 6 2
owner Jason Jason
type OatmealRaisin Sugar

GROUPING
>>>df.groupby('owner').sum()
count
owner
Jason 40
Marvin 2

>>>df.groupby(['type','owner']).sum()
count
type owner
ChocolateChip Jason 12
GingerMolassesJason 8
OatmealRaisin Jason 6
PeanutButter Jason 10
PowderSugar Jason 2
Sugar Jason 2
Marvin 2

RENAMING COLUMNS
>>>g_sum=df.groupby(['type']).sum()
>>>g_sum.columns=['Total']
Total
sum
ChocolateChip 12
GingerMolasses 8
OatmealRaisin 6
PeanutButter 10
PowderSugar 2
Sugar 4

PIVOT TABLES
>>>pd.pivot_table(df,values='count',index=['type'],columns=['owner'])
Owner Jason Marvin
type
ChocolateChip 12 NaN
GingerMolasses 8 NaN
OatmealRaisin 6 NaN
PeanutButter 10 NaN
PowderSugar 2 NaN
Sugar 2 2

JOINING
>>>df=pivot_t.join(g_sum)
>>>df.fillna(0,inplace=True)
Jason Marvin Total
type
ChocolateChip 12 0 12
GingerMolasses 8 0 8
OatmealRaisin 6 0 6
PeanutButter 10 0 10
PowderSugar 2 0 2
Sugar 2 2 4

OUR DATASOURCE
2014-06-2417:20:23.014642,0,34,102,0,0,0,60
2014-06-2417:25:01.176772,0,32,174,0,0,0,133
2014-06-2417:30:01.370235,0,28,57,0,0,0,75
2014-07-2114:35:01.797838,0,39,74,0,0,0,30,0,262,2,3,3,0
2014-07-2114:40:02.000434,0,54,143,0,0,0,44,0,499,3,9,9,0

READING FROM A CSV
df=pd.read_csv('results.csv',header=0,quotechar=''')

datetime abuse_passthrough any_abuse_handled ...
0 2014-06-2417:20:23.014642 0 34 ...

SETTING THE DATETIME AS THE INDEX
>>>df['datetime']=pandas.to_datetime(df.datetime)
>>>df.index=df.datetime
>>>deldf['datetime']
abuse_passthrough any_abuse_handled...
datetime ...
2014-06-2417:20:23.014642 0 34...
2014-06-2417:25:01.176772 0 32...

TIME SLICING
>>>df['2014-07-2113:55:00':'2014-07-2114:10:00']
datetime ...
2014-07-2113:55:01.153706 0 24...
2014-07-2114:00:01.372624 0 24...
2014-07-2114:05:01.910827 0 32...

HandlingMissingDataPoints
>>>df.fillna(0,inplace=True)

FUNCTIONS
>>>df.sum()
abuse_passthrough 39
any_abuse_handled 81537
handle_bp_message_handled 271689
handle_bp_message_corrupt_handled 0
error 0
forward_all_unhandled 0
original_message_handled 136116
list_unsubscribe_optout 71
default_handler_dropped 1342285
default_unhandled 2978
default_opt_out_bounce 22044
default_opt_out 23132
default_handler_pattern_dropped 0
dtype:float64

>>>df.mean()
abuse_passthrough 0.009673
any_abuse_handled 20.222470
handle_bp_message_handled 67.383185
handle_bp_message_corrupt_handled 0.000000
error 0.000000
forward_all_unhandled 0.000000
original_message_handled 33.758929
list_unsubscribe_optout 0.017609
default_handler_dropped 332.907986
default_unhandled 0.738591
default_opt_out_bounce 5.467262
default_opt_out 5.737103
default_handler_pattern_dropped 0.000000
dtype:float64

>>>df['2014-07-2113:55:00':'2014-07-2114:10:00'].apply(np.cumsum)
datetime ...
2014-07-2113:55:01.153706 0 24...
2014-07-2114:00:01.372624 0 48...
2014-07-2114:05:01.910827 0 80...

RESAMPLING
>>>d_df=df.resample('1D',how='sum')
datetime ...
2014-07-07 0 3178...
2014-07-08 1 6536...
2014-07-09 2 6857...

SORTING
>>>d_df.sort('any_abuse_handled',ascending=False)
datetime ...
2014-07-15 21 7664...
2014-07-17 5 7548...
2014-07-10 0 7106...
2014-07-11 10 6942...

DESCRIBE
>>>d_df.describe()
count 15.00000 15.000000...
mean 2.60000 5435.800000...
std 5.79162 1848.716358...
min 0.00000 2174.000000...
25% 0.00000 3810.000000...
50% 0.00000 6191.000000...
75% 1.50000 6899.500000...
max 21.00000 7664.000000...

OUTPUT TO CSV
>>>d_df.to_csv(path_or_buf='output.csv')

ONE MORE THING...
Vincent
importvincent

CHARTS
chart=vincent.StackedArea(d_df)
chart.legend(title='Legend')
chart.colors(brew='Set3')

QUESTIONS
JASON A MYERS / @JASONAMYERS

Introduction to Pandas

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