Cookbook

This is a repository for short and sweet examples and links for useful pandas recipes. We encourage users to add to this documentation.

Adding interesting links and/or inline examples to this section is a great First Pull Request.

Simplified, condensed, new-user friendly, in-line examples have been inserted where possible to augment the Stack-Overflow and GitHub links. Many of the links contain expanded information, above what the in-line examples offer.

Pandas (pd) and Numpy (np) are the only two abbreviated imported modules. The rest are kept explicitly imported for newer users.

These examples are written for python 3.4. Minor tweaks might be necessary for earlier python versions.

Idioms

These are some neat pandas idioms

if-then/if-then-else on one column, and assignment to another one or more columns:

In [1]: df = pd.DataFrame(
   ...:      {'AAA' : [4,5,6,7], 'BBB' : [10,20,30,40],'CCC' : [100,50,-30,-50]}); df
   ...: 
Out[1]: 
   AAA  BBB  CCC
0    4   10  100
1    5   20   50
2    6   30  -30
3    7   40  -50

if-then...

An if-then on one column

In [2]: df.ix[df.AAA >= 5,'BBB'] = -1; df
Out[2]: 
   AAA  BBB  CCC
0    4   10  100
1    5   -1   50
2    6   -1  -30
3    7   -1  -50

An if-then with assignment to 2 columns:

In [3]: df.ix[df.AAA >= 5,['BBB','CCC']] = 555; df
Out[3]: 
   AAA  BBB  CCC
0    4   10  100
1    5  555  555
2    6  555  555
3    7  555  555

Add another line with different logic, to do the -else

In [4]: df.ix[df.AAA < 5,['BBB','CCC']] = 2000; df
Out[4]: 
   AAA   BBB   CCC
0    4  2000  2000
1    5   555   555
2    6   555   555
3    7   555   555

Or use pandas where after you’ve set up a mask

In [5]: df_mask = pd.DataFrame({'AAA' : [True] * 4, 'BBB' : [False] * 4,'CCC' : [True,False] * 2})

In [6]: df.where(df_mask,-1000)
Out[6]: 
   AAA   BBB   CCC
0    4 -1000  2000
1    5 -1000 -1000
2    6 -1000   555
3    7 -1000 -1000

if-then-else using numpy’s where()

In [7]: df = pd.DataFrame(
   ...:      {'AAA' : [4,5,6,7], 'BBB' : [10,20,30,40],'CCC' : [100,50,-30,-50]}); df
   ...: 
Out[7]: 
   AAA  BBB  CCC
0    4   10  100
1    5   20   50
2    6   30  -30
3    7   40  -50

In [8]: df['logic'] = np.where(df['AAA'] > 5,'high','low'); df
Out[8]: 
   AAA  BBB  CCC logic
0    4   10  100   low
1    5   20   50   low
2    6   30  -30  high
3    7   40  -50  high

Splitting

Split a frame with a boolean criterion

In [9]: df = pd.DataFrame(
   ...:      {'AAA' : [4,5,6,7], 'BBB' : [10,20,30,40],'CCC' : [100,50,-30,-50]}); df
   ...: 
Out[9]: 
   AAA  BBB  CCC
0    4   10  100
1    5   20   50
2    6   30  -30
3    7   40  -50

In [10]: dflow = df[df.AAA <= 5]

In [11]: dfhigh = df[df.AAA > 5]

In [12]: dflow; dfhigh
Out[12]: 
   AAA  BBB  CCC
2    6   30  -30
3    7   40  -50

Building Criteria

Select with multi-column criteria

In [13]: df = pd.DataFrame(
   ....:      {'AAA' : [4,5,6,7], 'BBB' : [10,20,30,40],'CCC' : [100,50,-30,-50]}); df
   ....: 
Out[13]: 
   AAA  BBB  CCC
0    4   10  100
1    5   20   50
2    6   30  -30
3    7   40  -50

...and (without assignment returns a Series)

In [14]: newseries = df.loc[(df['BBB'] < 25) & (df['CCC'] >= -40), 'AAA']; newseries
Out[14]: 
0    4
1    5
Name: AAA, dtype: int64

...or (without assignment returns a Series)

In [15]: newseries = df.loc[(df['BBB'] > 25) | (df['CCC'] >= -40), 'AAA']; newseries;

...or (with assignment modifies the DataFrame.)

In [16]: df.loc[(df['BBB'] > 25) | (df['CCC'] >= 75), 'AAA'] = 0.1; df
Out[16]: 
   AAA  BBB  CCC
0  0.1   10  100
1  5.0   20   50
2  0.1   30  -30
3  0.1   40  -50

Select rows with data closest to certain value using argsort

In [17]: df = pd.DataFrame(
   ....:      {'AAA' : [4,5,6,7], 'BBB' : [10,20,30,40],'CCC' : [100,50,-30,-50]}); df
   ....: 
Out[17]: 
   AAA  BBB  CCC
0    4   10  100
1    5   20   50
2    6   30  -30
3    7   40  -50

In [18]: aValue = 43.0

In [19]: df.ix[(df.CCC-aValue).abs().argsort()]
Out[19]: 
   AAA  BBB  CCC
1    5   20   50
0    4   10  100
2    6   30  -30
3    7   40  -50

Dynamically reduce a list of criteria using a binary operators

In [20]: df = pd.DataFrame(
   ....:      {'AAA' : [4,5,6,7], 'BBB' : [10,20,30,40],'CCC' : [100,50,-30,-50]}); df
   ....: 
Out[20]: 
   AAA  BBB  CCC
0    4   10  100
1    5   20   50
2    6   30  -30
3    7   40  -50

In [21]: Crit1 = df.AAA <= 5.5

In [22]: Crit2 = df.BBB == 10.0

In [23]: Crit3 = df.CCC > -40.0

One could hard code:

In [24]: AllCrit = Crit1 & Crit2 & Crit3

...Or it can be done with a list of dynamically built criteria

In [25]: CritList = [Crit1,Crit2,Crit3]

In [26]: AllCrit = functools.reduce(lambda x,y: x & y, CritList)

In [27]: df[AllCrit]
Out[27]: 
   AAA  BBB  CCC
0    4   10  100

Selection

DataFrames

The indexing docs.

Using both row labels and value conditionals

In [28]: df = pd.DataFrame(
   ....:      {'AAA' : [4,5,6,7], 'BBB' : [10,20,30,40],'CCC' : [100,50,-30,-50]}); df
   ....: 
Out[28]: 
   AAA  BBB  CCC
0    4   10  100
1    5   20   50
2    6   30  -30
3    7   40  -50

In [29]: df[(df.AAA <= 6) & (df.index.isin([0,2,4]))]
Out[29]: 
   AAA  BBB  CCC
0    4   10  100
2    6   30  -30

Use loc for label-oriented slicing and iloc positional slicing

In [30]: data = {'AAA' : [4,5,6,7], 'BBB' : [10,20,30,40],'CCC' : [100,50,-30,-50]}

In [31]: df = pd.DataFrame(data=data,index=['foo','bar','boo','kar']); df
Out[31]: 
     AAA  BBB  CCC
foo    4   10  100
bar    5   20   50
boo    6   30  -30
kar    7   40  -50

There are 2 explicit slicing methods, with a third general case

  1. Positional-oriented (Python slicing style : exclusive of end)
  2. Label-oriented (Non-Python slicing style : inclusive of end)
  3. General (Either slicing style : depends on if the slice contains labels or positions)
In [32]: df.loc['bar':'kar'] #Label
Out[32]: 
     AAA  BBB  CCC
bar    5   20   50
boo    6   30  -30
kar    7   40  -50

#Generic
In [33]: df.ix[0:3] #Same as .iloc[0:3]
Out[33]: 
     AAA  BBB  CCC
foo    4   10  100
bar    5   20   50
boo    6   30  -30

In [34]: df.ix['bar':'kar'] #Same as .loc['bar':'kar']
Out[34]: 
     AAA  BBB  CCC
bar    5   20   50
boo    6   30  -30
kar    7   40  -50

Ambiguity arises when an index consists of integers with a non-zero start or non-unit increment.

In [35]: df2 = pd.DataFrame(data=data,index=[1,2,3,4]); #Note index starts at 1.

In [36]: df2.iloc[1:3] #Position-oriented
Out[36]: 
   AAA  BBB  CCC
2    5   20   50
3    6   30  -30

In [37]: df2.loc[1:3] #Label-oriented
Out[37]: 
   AAA  BBB  CCC
1    4   10  100
2    5   20   50
3    6   30  -30

In [38]: df2.ix[1:3] #General, will mimic loc (label-oriented)
Out[38]: 
   AAA  BBB  CCC
1    4   10  100
2    5   20   50
3    6   30  -30

In [39]: df2.ix[0:3] #General, will mimic iloc (position-oriented), as loc[0:3] would raise a KeyError
Out[39]: 
   AAA  BBB  CCC
1    4   10  100
2    5   20   50
3    6   30  -30

Using inverse operator (~) to take the complement of a mask

In [40]: df = pd.DataFrame(
   ....:      {'AAA' : [4,5,6,7], 'BBB' : [10,20,30,40], 'CCC' : [100,50,-30,-50]}); df
   ....: 
Out[40]: 
   AAA  BBB  CCC
0    4   10  100
1    5   20   50
2    6   30  -30
3    7   40  -50

In [41]: df[~((df.AAA <= 6) & (df.index.isin([0,2,4])))]
Out[41]: 
   AAA  BBB  CCC
1    5   20   50
3    7   40  -50

Panels

Extend a panel frame by transposing, adding a new dimension, and transposing back to the original dimensions

In [42]: rng = pd.date_range('1/1/2013',periods=100,freq='D')

In [43]: data = np.random.randn(100, 4)

In [44]: cols = ['A','B','C','D']

In [45]: df1, df2, df3 = pd.DataFrame(data, rng, cols), pd.DataFrame(data, rng, cols), pd.DataFrame(data, rng, cols)

In [46]: pf = pd.Panel({'df1':df1,'df2':df2,'df3':df3});pf
Out[46]: 
<class 'pandas.core.panel.Panel'>
Dimensions: 3 (items) x 100 (major_axis) x 4 (minor_axis)
Items axis: df1 to df3
Major_axis axis: 2013-01-01 00:00:00 to 2013-04-10 00:00:00
Minor_axis axis: A to D

#Assignment using Transpose  (pandas < 0.15)
In [47]: pf = pf.transpose(2,0,1)

In [48]: pf['E'] = pd.DataFrame(data, rng, cols)

In [49]: pf = pf.transpose(1,2,0);pf
Out[49]: 
<class 'pandas.core.panel.Panel'>
Dimensions: 3 (items) x 100 (major_axis) x 5 (minor_axis)
Items axis: df1 to df3
Major_axis axis: 2013-01-01 00:00:00 to 2013-04-10 00:00:00
Minor_axis axis: A to E

#Direct assignment (pandas > 0.15)
In [50]: pf.loc[:,:,'F'] = pd.DataFrame(data, rng, cols);pf
Out[50]: 
<class 'pandas.core.panel.Panel'>
Dimensions: 3 (items) x 100 (major_axis) x 6 (minor_axis)
Items axis: df1 to df3
Major_axis axis: 2013-01-01 00:00:00 to 2013-04-10 00:00:00
Minor_axis axis: A to F

Mask a panel by using np.where and then reconstructing the panel with the new masked values

New Columns

Efficiently and dynamically creating new columns using applymap

In [51]: df = pd.DataFrame(
   ....:      {'AAA' : [1,2,1,3], 'BBB' : [1,1,2,2], 'CCC' : [2,1,3,1]}); df
   ....: 
Out[51]: 
   AAA  BBB  CCC
0    1    1    2
1    2    1    1
2    1    2    3
3    3    2    1

In [52]: source_cols = df.columns # or some subset would work too.

In [53]: new_cols = [str(x) + "_cat" for x in source_cols]

In [54]: categories = {1 : 'Alpha', 2 : 'Beta', 3 : 'Charlie' }

In [55]: df[new_cols] = df[source_cols].applymap(categories.get);df
Out[55]: 
   AAA  BBB  CCC  AAA_cat BBB_cat  CCC_cat
0    1    1    2    Alpha   Alpha     Beta
1    2    1    1     Beta   Alpha    Alpha
2    1    2    3    Alpha    Beta  Charlie
3    3    2    1  Charlie    Beta    Alpha

Keep other columns when using min() with groupby

In [56]: df = pd.DataFrame(
   ....:      {'AAA' : [1,1,1,2,2,2,3,3], 'BBB' : [2,1,3,4,5,1,2,3]}); df
   ....: 
Out[56]: 
   AAA  BBB
0    1    2
1    1    1
2    1    3
3    2    4
4    2    5
5    2    1
6    3    2
7    3    3

Method 1 : idxmin() to get the index of the mins

In [57]: df.loc[df.groupby("AAA")["BBB"].idxmin()]
Out[57]: 
   AAA  BBB
1    1    1
5    2    1
6    3    2

Method 2 : sort then take first of each

In [58]: df.sort("BBB").groupby("AAA", as_index=False).first()
Out[58]: 
   AAA  BBB
0    1    1
1    2    1
2    3    2

Notice the same results, with the exception of the index.

MultiIndexing

The multindexing docs.

Creating a multi-index from a labeled frame

In [59]: df = pd.DataFrame({'row' : [0,1,2],
   ....:                    'One_X' : [1.1,1.1,1.1],
   ....:                    'One_Y' : [1.2,1.2,1.2],
   ....:                    'Two_X' : [1.11,1.11,1.11],
   ....:                    'Two_Y' : [1.22,1.22,1.22]}); df
   ....: 
Out[59]: 
   One_X  One_Y  Two_X  Two_Y  row
0    1.1    1.2   1.11   1.22    0
1    1.1    1.2   1.11   1.22    1
2    1.1    1.2   1.11   1.22    2

# As Labelled Index
In [60]: df = df.set_index('row');df
Out[60]: 
     One_X  One_Y  Two_X  Two_Y
row                            
0      1.1    1.2   1.11   1.22
1      1.1    1.2   1.11   1.22
2      1.1    1.2   1.11   1.22

# With Heirarchical Columns
In [61]: df.columns = pd.MultiIndex.from_tuples([tuple(c.split('_')) for c in df.columns]);df
Out[61]: 
     One        Two      
       X    Y     X     Y
row                      
0    1.1  1.2  1.11  1.22
1    1.1  1.2  1.11  1.22
2    1.1  1.2  1.11  1.22

# Now stack & Reset
In [62]: df = df.stack(0).reset_index(1);df
Out[62]: 
    level_1     X     Y
row                    
0       One  1.10  1.20
0       Two  1.11  1.22
1       One  1.10  1.20
1       Two  1.11  1.22
2       One  1.10  1.20
2       Two  1.11  1.22

# And fix the labels (Notice the label 'level_1' got added automatically)
In [63]: df.columns = ['Sample','All_X','All_Y'];df
Out[63]: 
    Sample  All_X  All_Y
row                     
0      One   1.10   1.20
0      Two   1.11   1.22
1      One   1.10   1.20
1      Two   1.11   1.22
2      One   1.10   1.20
2      Two   1.11   1.22

Arithmetic

Performing arithmetic with a multi-index that needs broadcasting

In [64]: cols = pd.MultiIndex.from_tuples([ (x,y) for x in ['A','B','C'] for y in ['O','I']])

In [65]: df = pd.DataFrame(np.random.randn(2,6),index=['n','m'],columns=cols); df
Out[65]: 
          A                   B                   C          
          O         I         O         I         O         I
n  1.920906 -0.388231 -2.314394  0.665508  0.402562  0.399555
m -1.765956  0.850423  0.388054  0.992312  0.744086 -0.739776

In [66]: df = df.div(df['C'],level=1); df
Out[66]: 
          A                   B            C   
          O         I         O         I  O  I
n  4.771702 -0.971660 -5.749162  1.665625  1  1
m -2.373321 -1.149568  0.521518 -1.341367  1  1

Slicing

Slicing a multi-index with xs

In [67]: coords = [('AA','one'),('AA','six'),('BB','one'),('BB','two'),('BB','six')]

In [68]: index = pd.MultiIndex.from_tuples(coords)

In [69]: df = pd.DataFrame([11,22,33,44,55],index,['MyData']); df
Out[69]: 
        MyData
AA one      11
   six      22
BB one      33
   two      44
   six      55

To take the cross section of the 1st level and 1st axis the index:

In [70]: df.xs('BB',level=0,axis=0)  #Note : level and axis are optional, and default to zero
Out[70]: 
     MyData
one      33
two      44
six      55

...and now the 2nd level of the 1st axis.

In [71]: df.xs('six',level=1,axis=0)
Out[71]: 
    MyData
AA      22
BB      55

Slicing a multi-index with xs, method #2

In [72]: index = list(itertools.product(['Ada','Quinn','Violet'],['Comp','Math','Sci']))

In [73]: headr = list(itertools.product(['Exams','Labs'],['I','II']))

In [74]: indx = pd.MultiIndex.from_tuples(index,names=['Student','Course'])

In [75]: cols = pd.MultiIndex.from_tuples(headr) #Notice these are un-named

In [76]: data = [[70+x+y+(x*y)%3 for x in range(4)] for y in range(9)]

In [77]: df = pd.DataFrame(data,indx,cols); df
Out[77]: 
               Exams     Labs    
                   I  II    I  II
Student Course                   
Ada     Comp      70  71   72  73
        Math      71  73   75  74
        Sci       72  75   75  75
Quinn   Comp      73  74   75  76
        Math      74  76   78  77
        Sci       75  78   78  78
Violet  Comp      76  77   78  79
        Math      77  79   81  80
        Sci       78  81   81  81

In [78]: All = slice(None)

In [79]: df.loc['Violet']
Out[79]: 
       Exams     Labs    
           I  II    I  II
Course                   
Comp      76  77   78  79
Math      77  79   81  80
Sci       78  81   81  81

In [80]: df.loc[(All,'Math'),All]
Out[80]: 
               Exams     Labs    
                   I  II    I  II
Student Course                   
Ada     Math      71  73   75  74
Quinn   Math      74  76   78  77
Violet  Math      77  79   81  80

In [81]: df.loc[(slice('Ada','Quinn'),'Math'),All]
Out[81]: 
               Exams     Labs    
                   I  II    I  II
Student Course                   
Ada     Math      71  73   75  74
Quinn   Math      74  76   78  77

In [82]: df.loc[(All,'Math'),('Exams')]
Out[82]: 
                 I  II
Student Course        
Ada     Math    71  73
Quinn   Math    74  76
Violet  Math    77  79

In [83]: df.loc[(All,'Math'),(All,'II')]
Out[83]: 
               Exams Labs
                  II   II
Student Course           
Ada     Math      73   74
Quinn   Math      76   77
Violet  Math      79   80

Setting portions of a multi-index with xs

Sorting

Sort by specific column or an ordered list of columns, with a multi-index

In [84]: df.sort(('Labs', 'II'), ascending=False)
Out[84]: 
               Exams     Labs    
                   I  II    I  II
Student Course                   
Violet  Sci       78  81   81  81
        Math      77  79   81  80
        Comp      76  77   78  79
Quinn   Sci       75  78   78  78
        Math      74  76   78  77
        Comp      73  74   75  76
Ada     Sci       72  75   75  75
        Math      71  73   75  74
        Comp      70  71   72  73

Partial Selection, the need for sortedness;

panelnd

The panelnd docs.

Construct a 5D panelnd

Missing Data

The missing data docs.

Fill forward a reversed timeseries

In [85]: df = pd.DataFrame(np.random.randn(6,1), index=pd.date_range('2013-08-01', periods=6, freq='B'), columns=list('A'))

In [86]: df.ix[3,'A'] = np.nan

In [87]: df
Out[87]: 
                   A
2013-08-01 -1.054874
2013-08-02 -0.179642
2013-08-05  0.639589
2013-08-06       NaN
2013-08-07  1.906684
2013-08-08  0.104050

In [88]: df.reindex(df.index[::-1]).ffill()
Out[88]: 
                   A
2013-08-08  0.104050
2013-08-07  1.906684
2013-08-06  1.906684
2013-08-05  0.639589
2013-08-02 -0.179642
2013-08-01 -1.054874

cumsum reset at NaN values

Grouping

The grouping docs.

Basic grouping with apply

Unlike agg, apply’s callable is passed a sub-DataFrame which gives you access to all the columns

In [89]: df = pd.DataFrame({'animal': 'cat dog cat fish dog cat cat'.split(),
   ....:                    'size': list('SSMMMLL'),
   ....:                    'weight': [8, 10, 11, 1, 20, 12, 12],
   ....:                    'adult' : [False] * 5 + [True] * 2}); df
   ....: 
Out[89]: 
   adult animal size  weight
0  False    cat    S       8
1  False    dog    S      10
2  False    cat    M      11
3  False   fish    M       1
4  False    dog    M      20
5   True    cat    L      12
6   True    cat    L      12

#List the size of the animals with the highest weight.
In [90]: df.groupby('animal').apply(lambda subf: subf['size'][subf['weight'].idxmax()])
Out[90]: 
animal
cat       L
dog       M
fish      M
dtype: object

Using get_group

In [91]: gb = df.groupby(['animal'])

In [92]: gb.get_group('cat')
Out[92]: 
   adult animal size  weight
0  False    cat    S       8
2  False    cat    M      11
5   True    cat    L      12
6   True    cat    L      12

Apply to different items in a group

In [93]: def GrowUp(x):
   ....:    avg_weight = sum(x[x.size == 'S'].weight * 1.5)
   ....:    avg_weight += sum(x[x.size == 'M'].weight * 1.25)
   ....:    avg_weight += sum(x[x.size == 'L'].weight)
   ....:    avg_weight = avg_weight / len(x)
   ....:    return pd.Series(['L',avg_weight,True], index=['size', 'weight', 'adult'])
   ....: 

In [94]: expected_df = gb.apply(GrowUp)

In [95]: expected_df
Out[95]: 
       size   weight adult
animal                    
cat       L  12.4375  True
dog       L  20.0000  True
fish      L   1.2500  True

Expanding Apply

In [96]: S = pd.Series([i / 100.0 for i in range(1,11)])

In [97]: def CumRet(x,y):
   ....:    return x * (1 + y)
   ....: 

In [98]: def Red(x):
   ....:    return functools.reduce(CumRet,x,1.0)
   ....: 

In [99]: pd.expanding_apply(S, Red)
Out[99]: 
0    1.010000
1    1.030200
2    1.061106
3    1.103550
4    1.158728
5    1.228251
6    1.314229
7    1.419367
8    1.547110
9    1.701821
dtype: float64

Replacing some values with mean of the rest of a group

In [100]: df = pd.DataFrame({'A' : [1, 1, 2, 2], 'B' : [1, -1, 1, 2]})

In [101]: gb = df.groupby('A')

In [102]: def replace(g):
   .....:    mask = g < 0
   .....:    g.loc[mask] = g[~mask].mean()
   .....:    return g
   .....: 

In [103]: gb.transform(replace)
Out[103]: 
   B
0  1
1  1
2  1
3  2

Sort groups by aggregated data

In [104]: df = pd.DataFrame({'code': ['foo', 'bar', 'baz'] * 2,
   .....:                    'data': [0.16, -0.21, 0.33, 0.45, -0.59, 0.62],
   .....:                    'flag': [False, True] * 3})
   .....: 

In [105]: code_groups = df.groupby('code')

In [106]: agg_n_sort_order = code_groups[['data']].transform(sum).sort('data')

In [107]: sorted_df = df.ix[agg_n_sort_order.index]

In [108]: sorted_df
Out[108]: 
  code  data   flag
1  bar -0.21   True
4  bar -0.59  False
0  foo  0.16  False
3  foo  0.45   True
2  baz  0.33  False
5  baz  0.62   True

Create multiple aggregated columns

In [109]: rng = pd.date_range(start="2014-10-07",periods=10,freq='2min')

In [110]: ts = pd.Series(data = list(range(10)), index = rng)

In [111]: def MyCust(x):
   .....:    if len(x) > 2:
   .....:       return x[1] * 1.234
   .....:    return pd.NaT
   .....: 

In [112]: mhc = {'Mean' : np.mean, 'Max' : np.max, 'Custom' : MyCust}

In [113]: ts.resample("5min",how = mhc)
Out[113]: 
                     Max Custom  Mean
2014-10-07 00:00:00    2  1.234   1.0
2014-10-07 00:05:00    4    NaN   3.5
2014-10-07 00:10:00    7  7.404   6.0
2014-10-07 00:15:00    9    NaN   8.5

In [114]: ts
Out[114]: 
2014-10-07 00:00:00    0
2014-10-07 00:02:00    1
2014-10-07 00:04:00    2
2014-10-07 00:06:00    3
2014-10-07 00:08:00    4
2014-10-07 00:10:00    5
2014-10-07 00:12:00    6
2014-10-07 00:14:00    7
2014-10-07 00:16:00    8
2014-10-07 00:18:00    9
Freq: 2T, dtype: int64

Create a value counts column and reassign back to the DataFrame

In [115]: df = pd.DataFrame({'Color': 'Red Red Red Blue'.split(),
   .....:                    'Value': [100, 150, 50, 50]}); df
   .....: 
Out[115]: 
  Color  Value
0   Red    100
1   Red    150
2   Red     50
3  Blue     50

In [116]: df['Counts'] = df.groupby(['Color']).transform(len)

In [117]: df
Out[117]: 
  Color  Value  Counts
0   Red    100       3
1   Red    150       3
2   Red     50       3
3  Blue     50       1

Shift groups of the values in a column based on the index

In [118]: df = pd.DataFrame(
   .....:    {u'line_race': [10, 10, 8, 10, 10, 8],
   .....:     u'beyer': [99, 102, 103, 103, 88, 100]},
   .....:     index=[u'Last Gunfighter', u'Last Gunfighter', u'Last Gunfighter',
   .....:            u'Paynter', u'Paynter', u'Paynter']); df
   .....: 
Out[118]: 
                 beyer  line_race
Last Gunfighter     99         10
Last Gunfighter    102         10
Last Gunfighter    103          8
Paynter            103         10
Paynter             88         10
Paynter            100          8

In [119]: df['beyer_shifted'] = df.groupby(level=0)['beyer'].shift(1)

In [120]: df
Out[120]: 
                 beyer  line_race  beyer_shifted
Last Gunfighter     99         10            NaN
Last Gunfighter    102         10             99
Last Gunfighter    103          8            102
Paynter            103         10            NaN
Paynter             88         10            103
Paynter            100          8             88

Splitting

Splitting a frame

Create a list of dataframes, split using a delineation based on logic included in rows.

In [121]: df = pd.DataFrame(data={'Case' : ['A','A','A','B','A','A','B','A','A'],
   .....:                         'Data' : np.random.randn(9)})
   .....: 

In [122]: dfs = list(zip(*df.groupby(pd.rolling_median((1*(df['Case']=='B')).cumsum(),3,True))))[-1]

In [123]: dfs[0]
Out[123]: 
  Case      Data
0    A  0.174068
1    A -0.439461
2    A -0.741343
3    B -0.079673

In [124]: dfs[1]
Out[124]: 
  Case      Data
4    A -0.922875
5    A  0.303638
6    B -0.917368

In [125]: dfs[2]
Out[125]: 
  Case      Data
7    A -1.624062
8    A -0.758514

Pivot

The Pivot docs.

Partial sums and subtotals

In [126]: df = pd.DataFrame(data={'Province' : ['ON','QC','BC','AL','AL','MN','ON'],
   .....:                          'City' : ['Toronto','Montreal','Vancouver','Calgary','Edmonton','Winnipeg','Windsor'],
   .....:                          'Sales' : [13,6,16,8,4,3,1]})
   .....: 

In [127]: table = pd.pivot_table(df,values=['Sales'],index=['Province'],columns=['City'],aggfunc=np.sum,margins=True)

In [128]: table.stack('City')
Out[128]: 
                    Sales
Province City            
AL       All           12
         Calgary        8
         Edmonton       4
BC       All           16
         Vancouver     16
MN       All            3
         Winnipeg       3
...                   ...
All      Calgary        8
         Edmonton       4
         Montreal       6
         Toronto       13
         Vancouver     16
         Windsor        1
         Winnipeg       3

[20 rows x 1 columns]

Frequency table like plyr in R

In [129]: grades = [48,99,75,80,42,80,72,68,36,78]

In [130]: df = pd.DataFrame( {'ID': ["x%d" % r for r in range(10)],
   .....:                     'Gender' : ['F', 'M', 'F', 'M', 'F', 'M', 'F', 'M', 'M', 'M'],
   .....:                     'ExamYear': ['2007','2007','2007','2008','2008','2008','2008','2009','2009','2009'],
   .....:                     'Class': ['algebra', 'stats', 'bio', 'algebra', 'algebra', 'stats', 'stats', 'algebra', 'bio', 'bio'],
   .....:                     'Participated': ['yes','yes','yes','yes','no','yes','yes','yes','yes','yes'],
   .....:                     'Passed': ['yes' if x > 50 else 'no' for x in grades],
   .....:                     'Employed': [True,True,True,False,False,False,False,True,True,False],
   .....:                     'Grade': grades})
   .....: 

In [131]: df.groupby('ExamYear').agg({'Participated': lambda x: x.value_counts()['yes'],
   .....:                     'Passed': lambda x: sum(x == 'yes'),
   .....:                     'Employed' : lambda x : sum(x),
   .....:                     'Grade' : lambda x : sum(x) / len(x)})
   .....: 
Out[131]: 
          Grade  Employed  Participated  Passed
ExamYear                                       
2007         74         3             3       2
2008         68         0             3       3
2009         60         2             3       2

Apply

Rolling Apply to Organize - Turning embedded lists into a multi-index frame

In [132]: df = pd.DataFrame(data={'A' : [[2,4,8,16],[100,200],[10,20,30]], 'B' : [['a','b','c'],['jj','kk'],['ccc']]},index=['I','II','III'])

In [133]: def SeriesFromSubList(aList):
   .....:    return pd.Series(aList)
   .....: 

In [134]: df_orgz = pd.concat(dict([ (ind,row.apply(SeriesFromSubList)) for ind,row in df.iterrows() ]))

Rolling Apply with a DataFrame returning a Series

Rolling Apply to multiple columns where function calculates a Series before a Scalar from the Series is returned

In [135]: df = pd.DataFrame(data=np.random.randn(2000,2)/10000,
   .....:                   index=pd.date_range('2001-01-01',periods=2000),
   .....:                   columns=['A','B']); df
   .....: 
Out[135]: 
                   A         B
2001-01-01 -0.000056 -0.000059
2001-01-02 -0.000107 -0.000168
2001-01-03  0.000040  0.000061
2001-01-04  0.000039  0.000182
2001-01-05  0.000071 -0.000067
2001-01-06  0.000024  0.000031
2001-01-07  0.000012 -0.000021
...              ...       ...
2006-06-17  0.000129  0.000094
2006-06-18  0.000059  0.000216
2006-06-19 -0.000069  0.000283
2006-06-20  0.000089  0.000084
2006-06-21  0.000075  0.000041
2006-06-22 -0.000037 -0.000011
2006-06-23 -0.000070 -0.000048

[2000 rows x 2 columns]

In [136]: def gm(aDF,Const):
   .....:    v = ((((aDF.A+aDF.B)+1).cumprod())-1)*Const
   .....:    return (aDF.index[0],v.iloc[-1])
   .....: 

In [137]: S = pd.Series(dict([ gm(df.iloc[i:min(i+51,len(df)-1)],5) for i in range(len(df)-50) ])); S
Out[137]: 
2001-01-01   -0.003108
2001-01-02   -0.001787
2001-01-03    0.000204
2001-01-04   -0.000166
2001-01-05   -0.002148
...
2006-04-29   -0.006728
2006-04-30   -0.005840
2006-05-01   -0.003650
2006-05-02   -0.003801
2006-05-03   -0.004272
2006-05-04   -0.003839
Length: 1950

Rolling apply with a DataFrame returning a Scalar

Rolling Apply to multiple columns where function returns a Scalar (Volume Weighted Average Price)

In [138]: rng = pd.date_range(start = '2014-01-01',periods = 100)

In [139]: df = pd.DataFrame({'Open' : np.random.randn(len(rng)),
   .....:                    'Close' : np.random.randn(len(rng)),
   .....:                    'Volume' : np.random.randint(100,2000,len(rng))}, index=rng); df
   .....: 
Out[139]: 
               Close      Open  Volume
2014-01-01  1.550590  0.458513    1371
2014-01-02 -0.818812 -0.508850    1433
2014-01-03  1.160619  0.257610     645
2014-01-04  0.081521 -1.773393     878
2014-01-05  1.083284 -0.560676    1143
2014-01-06 -0.518721  0.284174    1088
2014-01-07  0.140661  1.146889    1722
...              ...       ...     ...
2014-04-04  0.458193 -0.669474    1768
2014-04-05  0.108502 -1.616315     836
2014-04-06  1.418082 -1.294906     694
2014-04-07  0.486530  1.171647     796
2014-04-08  0.181885  0.501639     265
2014-04-09 -0.707238 -0.361868    1293
2014-04-10  1.211432  1.564429    1088

[100 rows x 3 columns]

In [140]: def vwap(bars): return ((bars.Close*bars.Volume).sum()/bars.Volume.sum()).round(2)

In [141]: window = 5

In [142]: s = pd.concat([ (pd.Series(vwap(df.iloc[i:i+window]), index=[df.index[i+window]])) for i in range(len(df)-window) ]); s
Out[142]: 
2014-01-06    0.55
2014-01-07    0.06
2014-01-08    0.32
2014-01-09    0.03
2014-01-10    0.08
...
2014-04-05    0.48
2014-04-06    0.54
2014-04-07    0.46
2014-04-08    0.45
2014-04-09    0.53
2014-04-10    0.15
Length: 95

Timeseries

Between times

Using indexer between time

Constructing a datetime range that excludes weekends and includes only certain times

Vectorized Lookup

Turn a matrix with hours in columns and days in rows into a continuous row sequence in the form of a time series. How to rearrange a python pandas DataFrame?

Dealing with duplicates when reindexing a timeseries to a specified frequency

Calculate the first day of the month for each entry in a DatetimeIndex

In [143]: dates = pd.date_range('2000-01-01', periods=5)

In [144]: dates.to_period(freq='M').to_timestamp()
Out[144]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2000-01-01, ..., 2000-01-01]
Length: 5, Freq: None, Timezone: None

Merge

The Concat docs. The Join docs.

Append two dataframes with overlapping index (emulate R rbind)

In [145]: rng = pd.date_range('2000-01-01', periods=6)

In [146]: df1 = pd.DataFrame(np.random.randn(6, 3), index=rng, columns=['A', 'B', 'C'])

In [147]: df2 = df1.copy()

ignore_index is needed in pandas < v0.13, and depending on df construction

In [148]: df = df1.append(df2,ignore_index=True); df
Out[148]: 
           A         B         C
0  -0.174202 -0.477257  0.239870
1  -0.654455 -1.411456 -1.778457
2   0.351578  0.307871 -0.286865
3   0.565398 -0.185821  0.937593
4   0.446473  0.566368  0.721476
5   1.710685 -0.667054 -0.651191
6  -0.174202 -0.477257  0.239870
7  -0.654455 -1.411456 -1.778457
8   0.351578  0.307871 -0.286865
9   0.565398 -0.185821  0.937593
10  0.446473  0.566368  0.721476
11  1.710685 -0.667054 -0.651191

Self Join of a DataFrame

In [149]: df = pd.DataFrame(data={'Area' : ['A'] * 5 + ['C'] * 2,
   .....:                         'Bins' : [110] * 2 + [160] * 3 + [40] * 2,
   .....:                         'Test_0' : [0, 1, 0, 1, 2, 0, 1],
   .....:                         'Data' : np.random.randn(7)});df
   .....: 
Out[149]: 
  Area  Bins      Data  Test_0
0    A   110 -0.399974       0
1    A   110 -1.519206       1
2    A   160  1.678487       0
3    A   160  0.005345       1
4    A   160 -0.534461       2
5    C    40  0.255077       0
6    C    40  1.093310       1

In [150]: df['Test_1'] = df['Test_0'] - 1

In [151]: pd.merge(df, df, left_on=['Bins', 'Area','Test_0'], right_on=['Bins', 'Area','Test_1'],suffixes=('_L','_R'))
Out[151]: 
  Area  Bins    Data_L  Test_0_L  Test_1_L    Data_R  Test_0_R  Test_1_R
0    A   110 -0.399974         0        -1 -1.519206         1         0
1    A   160  1.678487         0        -1  0.005345         1         0
2    A   160  0.005345         1         0 -0.534461         2         1
3    C    40  0.255077         0        -1  1.093310         1         0

How to set the index and join

KDB like asof join

Join with a criteria based on the values

Plotting

The Plotting docs.

Make Matplotlib look like R

Setting x-axis major and minor labels

Plotting multiple charts in an ipython notebook

Creating a multi-line plot

Plotting a heatmap

Annotate a time-series plot

Annotate a time-series plot #2

Generate Embedded plots in excel files using Pandas, Vincent and xlsxwriter

Boxplot for each quartile of a stratifying variable

In [152]: df = pd.DataFrame(
   .....:      {u'stratifying_var': np.random.uniform(0, 100, 20),
   .....:       u'price': np.random.normal(100, 5, 20)})
   .....: 

In [153]: df[u'quartiles'] = pd.qcut(
   .....:     df[u'stratifying_var'],
   .....:     4,
   .....:     labels=[u'0-25%', u'25-50%', u'50-75%', u'75-100%'])
   .....: 

In [154]: df.boxplot(column=u'price', by=u'quartiles')
Out[154]: <matplotlib.axes._subplots.AxesSubplot at 0xafa8550c>
_images/quartile_boxplot.png

Data In/Out

Performance comparison of SQL vs HDF5

CSV

The CSV docs

read_csv in action

appending to a csv

how to read in multiple files, appending to create a single dataframe

Reading a csv chunk-by-chunk

Reading only certain rows of a csv chunk-by-chunk

Reading the first few lines of a frame

Reading a file that is compressed but not by gzip/bz2 (the native compressed formats which read_csv understands). This example shows a WinZipped file, but is a general application of opening the file within a context manager and using that handle to read. See here

Inferring dtypes from a file

Dealing with bad lines

Dealing with bad lines II

Reading CSV with Unix timestamps and converting to local timezone

Write a multi-row index CSV without writing duplicates

Parsing date components in multi-columns is faster with a format

In [30]: i = pd.date_range('20000101',periods=10000)

In [31]: df = pd.DataFrame(dict(year = i.year, month = i.month, day = i.day))

In [32]: df.head()
Out[32]:
   day  month  year
0    1      1  2000
1    2      1  2000
2    3      1  2000
3    4      1  2000
4    5      1  2000

In [33]: %timeit pd.to_datetime(df.year*10000+df.month*100+df.day,format='%Y%m%d')
100 loops, best of 3: 7.08 ms per loop

# simulate combinging into a string, then parsing
In [34]: ds = df.apply(lambda x: "%04d%02d%02d" % (x['year'],x['month'],x['day']),axis=1)

In [35]: ds.head()
Out[35]:
0    20000101
1    20000102
2    20000103
3    20000104
4    20000105
dtype: object

In [36]: %timeit pd.to_datetime(ds)
1 loops, best of 3: 488 ms per loop

HDFStore

The HDFStores docs

Simple Queries with a Timestamp Index

Managing heterogeneous data using a linked multiple table hierarchy

Merging on-disk tables with millions of rows

De-duplicating a large store by chunks, essentially a recursive reduction operation. Shows a function for taking in data from csv file and creating a store by chunks, with date parsing as well. See here

Creating a store chunk-by-chunk from a csv file

Appending to a store, while creating a unique index

Large Data work flows

Reading in a sequence of files, then providing a global unique index to a store while appending

Groupby on a HDFStore with low group density

Groupby on a HDFStore with high group density

Hierarchical queries on a HDFStore

Counting with a HDFStore

Troubleshoot HDFStore exceptions

Setting min_itemsize with strings

Using ptrepack to create a completely-sorted-index on a store

Storing Attributes to a group node

In [155]: df = pd.DataFrame(np.random.randn(8,3))

In [156]: store = pd.HDFStore('test.h5')

In [157]: store.put('df',df)

# you can store an arbitrary python object via pickle
In [158]: store.get_storer('df').attrs.my_attribute = dict(A = 10)

In [159]: store.get_storer('df').attrs.my_attribute
Out[159]: {'A': 10}

Binary Files

pandas readily accepts numpy record arrays, if you need to read in a binary file consisting of an array of C structs. For example, given this C program in a file called main.c compiled with gcc main.c -std=gnu99 on a 64-bit machine,

#include <stdio.h>
#include <stdint.h>

typedef struct _Data
{
    int32_t count;
    double avg;
    float scale;
} Data;

int main(int argc, const char *argv[])
{
    size_t n = 10;
    Data d[n];

    for (int i = 0; i < n; ++i)
    {
        d[i].count = i;
        d[i].avg = i + 1.0;
        d[i].scale = (float) i + 2.0f;
    }

    FILE *file = fopen("binary.dat", "wb");
    fwrite(&d, sizeof(Data), n, file);
    fclose(file);

    return 0;
}

the following Python code will read the binary file 'binary.dat' into a pandas DataFrame, where each element of the struct corresponds to a column in the frame:

names = 'count', 'avg', 'scale'

# note that the offsets are larger than the size of the type because of
# struct padding
offsets = 0, 8, 16
formats = 'i4', 'f8', 'f4'
dt = np.dtype({'names': names, 'offsets': offsets, 'formats': formats},
              align=True)
df = pd.DataFrame(np.fromfile('binary.dat', dt))

Note

The offsets of the structure elements may be different depending on the architecture of the machine on which the file was created. Using a raw binary file format like this for general data storage is not recommended, as it is not cross platform. We recommended either HDF5 or msgpack, both of which are supported by pandas’ IO facilities.

Timedeltas

The Timedeltas docs.

Using timedeltas

In [160]: s  = pd.Series(pd.date_range('2012-1-1', periods=3, freq='D'))

In [161]: s - s.max()
Out[161]: 
0   -2 days
1   -1 days
2    0 days
dtype: timedelta64[ns]

In [162]: s.max() - s
Out[162]: 
0   2 days
1   1 days
2   0 days
dtype: timedelta64[ns]

In [163]: s - datetime.datetime(2011,1,1,3,5)
Out[163]: 
0   364 days 20:55:00
1   365 days 20:55:00
2   366 days 20:55:00
dtype: timedelta64[ns]

In [164]: s + datetime.timedelta(minutes=5)
Out[164]: 
0   2012-01-01 00:05:00
1   2012-01-02 00:05:00
2   2012-01-03 00:05:00
dtype: datetime64[ns]

In [165]: datetime.datetime(2011,1,1,3,5) - s
Out[165]: 
0   -365 days +03:05:00
1   -366 days +03:05:00
2   -367 days +03:05:00
dtype: timedelta64[ns]

In [166]: datetime.timedelta(minutes=5) + s
Out[166]: 
0   2012-01-01 00:05:00
1   2012-01-02 00:05:00
2   2012-01-03 00:05:00
dtype: datetime64[ns]

Adding and subtracting deltas and dates

In [167]: deltas = pd.Series([ datetime.timedelta(days=i) for i in range(3) ])

In [168]: df = pd.DataFrame(dict(A = s, B = deltas)); df
Out[168]: 
           A      B
0 2012-01-01 0 days
1 2012-01-02 1 days
2 2012-01-03 2 days

In [169]: df['New Dates'] = df['A'] + df['B'];

In [170]: df['Delta'] = df['A'] - df['New Dates']; df
Out[170]: 
           A      B  New Dates   Delta
0 2012-01-01 0 days 2012-01-01  0 days
1 2012-01-02 1 days 2012-01-03 -1 days
2 2012-01-03 2 days 2012-01-05 -2 days

In [171]: df.dtypes
Out[171]: 
A             datetime64[ns]
B            timedelta64[ns]
New Dates     datetime64[ns]
Delta        timedelta64[ns]
dtype: object

Another example

Values can be set to NaT using np.nan, similar to datetime

In [172]: y = s - s.shift(); y
Out[172]: 
0      NaT
1   1 days
2   1 days
dtype: timedelta64[ns]

In [173]: y[1] = np.nan; y
Out[173]: 
0      NaT
1      NaT
2   1 days
dtype: timedelta64[ns]

Aliasing Axis Names

To globally provide aliases for axis names, one can define these 2 functions:

In [174]: def set_axis_alias(cls, axis, alias):
   .....:    if axis not in cls._AXIS_NUMBERS:
   .....:       raise Exception("invalid axis [%s] for alias [%s]" % (axis, alias))
   .....:    cls._AXIS_ALIASES[alias] = axis
   .....: 
In [175]: def clear_axis_alias(cls, axis, alias):
   .....:    if axis not in cls._AXIS_NUMBERS:
   .....:       raise Exception("invalid axis [%s] for alias [%s]" % (axis, alias))
   .....:    cls._AXIS_ALIASES.pop(alias,None)
   .....: 
In [176]: set_axis_alias(pd.DataFrame,'columns', 'myaxis2')

In [177]: df2 = pd.DataFrame(np.random.randn(3,2),columns=['c1','c2'],index=['i1','i2','i3'])

In [178]: df2.sum(axis='myaxis2')
Out[178]: 
i1    0.239786
i2    0.259018
i3    0.163470
dtype: float64

In [179]: clear_axis_alias(pd.DataFrame,'columns', 'myaxis2')

Creating Example Data

To create a dataframe from every combination of some given values, like R’s expand.grid() function, we can create a dict where the keys are column names and the values are lists of the data values:

In [180]: def expand_grid(data_dict):
   .....:    rows = itertools.product(*data_dict.values())
   .....:    return pd.DataFrame.from_records(rows, columns=data_dict.keys())
   .....: 

In [181]: df = expand_grid(
   .....:    {'height': [60, 70],
   .....:     'weight': [100, 140, 180],
   .....:     'sex': ['Male', 'Female']})
   .....: 

In [182]: df
Out[182]: 
       sex  weight  height
0     Male     100      60
1     Male     100      70
2     Male     140      60
3     Male     140      70
4     Male     180      60
5     Male     180      70
6   Female     100      60
7   Female     100      70
8   Female     140      60
9   Female     140      70
10  Female     180      60
11  Female     180      70