How to create a lagged data structure using pandas dataframe

Example

s=pd.Series([5,4,3,2,1], index=[1,2,3,4,5])
print s 
1    5
2    4
3    3
4    2
5    1

Is there an efficient way to create a series. e.g. containing in each row the lagged values (in this example up to lag 2)

3    [3, 4, 5]
4    [2, 3, 4]
5    [1, 2, 3]

This corresponds to s=pd.Series([[3,4,5],[2,3,4],[1,2,3]], index=[3,4,5])

How can this be done in an efficient way for dataframes with a lot of timeseries which are very long?

Thanks

Edited after seeing the answers

ok, at the end I implemented this function:

def buildLaggedFeatures(s,lag=2,dropna=True):
'''
Builds a new DataFrame to facilitate regressing over all possible lagged features
'''
if type(s) is pd.DataFrame:
    new_dict={}
    for col_name in s:
        new_dict[col_name]=s[col_name]
        # create lagged Series
        for l in range(1,lag+1):
            new_dict['%s_lag%d' %(col_name,l)]=s[col_name].shift(l)
    res=pd.DataFrame(new_dict,index=s.index)

elif type(s) is pd.Series:
    the_range=range(lag+1)
    res=pd.concat([s.shift(i) for i in the_range],axis=1)
    res.columns=['lag_%d' %i for i in the_range]
else:
    print 'Only works for DataFrame or Series'
    return None
if dropna:
    return res.dropna()
else:
    return res 

it produces the wished outputs and manages the naming of columns in the resulting DataFrame.

For a Series as input:

s=pd.Series([5,4,3,2,1], index=[1,2,3,4,5])
res=buildLaggedFeatures(s,lag=2,dropna=False)
   lag_0  lag_1  lag_2
1      5    NaN    NaN
2      4      5    NaN
3      3      4      5
4      2      3      4
5      1      2      3

and for a DataFrame as input:

s2=s=pd.DataFrame({'a':[5,4,3,2,1], 'b':[50,40,30,20,10]},index=[1,2,3,4,5])
res2=buildLaggedFeatures(s2,lag=2,dropna=True)

   a  a_lag1  a_lag2   b  b_lag1  b_lag2
3  3       4       5  30      40      50
4  2       3       4  20      30      40
5  1       2       3  10      20      30

As mentioned, it could be worth looking into the rolling_ functions, which will mean you won't have as many copies around.

One solution is to concat shifted Series together to make a DataFrame:

In [11]: pd.concat([s, s.shift(), s.shift(2)], axis=1)
Out[11]: 
   0   1   2
1  5 NaN NaN
2  4   5 NaN
3  3   4   5
4  2   3   4
5  1   2   3

In [12]: pd.concat([s, s.shift(), s.shift(2)], axis=1).dropna()
Out[12]: 
   0  1  2
3  3  4  5
4  2  3  4
5  1  2  3

Doing work on this will be more efficient that on lists...

Very simple solution using pandas DataFrame:

number_lags = 3
df = pd.DataFrame(data={'vals':[5,4,3,2,1]})
for lag in xrange(1, number_lags + 1):
    df['lag_' + str(lag)] = df.vals.shift(lag)

#if you want numpy arrays with no null values: 
df.dropna().values for numpy arrays

for Python 3.x (change xrange to range)

number_lags = 3
df = pd.DataFrame(data={'vals':[5,4,3,2,1]})
for lag in range(1, number_lags + 1):
    df['lag_' + str(lag)] = df.vals.shift(lag)

print(df)

   vals  lag_1  lag_2  lag_3
0     5    NaN    NaN    NaN
1     4    5.0    NaN    NaN
2     3    4.0    5.0    NaN
3     2    3.0    4.0    5.0
4     1    2.0    3.0    4.0

For a dataframe df with the lag to be applied on 'col name', you can use the shift function.

df['lag1']=df['col name'].shift(1)
df['lag2']=df['col name'].shift(2)

I like to put the lag numbers in the columns by making the columns a MultiIndex. This way, the names of the columns are retained.

Here's an example of the result:

# Setup
indx = pd.Index([1, 2, 3, 4, 5], name='time')
s=pd.Series(
    [5, 4, 3, 2, 1],
    index=indx,
    name='population')

shift_timeseries_by_lags(pd.DataFrame(s), [0, 1, 2])

Result: a MultiIndex DataFrame with two column labels: the original one ("population") and a new one ("lag"):

---

Solution: Like in the accepted solution, we use DataFrame.shift and then pandas.concat.

def shift_timeseries_by_lags(df, lags, lag_label='lag'):
    return pd.concat([
        shift_timeseries_and_create_multiindex_column(df, lag,
                                                      lag_label=lag_label)
        for lag in lags], axis=1)

def shift_timeseries_and_create_multiindex_column(
        dataframe, lag, lag_label='lag'):
    return (dataframe.shift(lag)
                     .pipe(append_level_to_columns_of_dataframe,
                           lag, lag_label))

I wish there were an easy way to append a list of labels to the existing columns. Here's my solution.

def append_level_to_columns_of_dataframe(
        dataframe, new_level, name_of_new_level, inplace=False):
    """Given a (possibly MultiIndex) DataFrame, append labels to the column
    labels and assign this new level a name.

    Parameters
    ----------
    dataframe : a pandas DataFrame with an Index or MultiIndex columns

    new_level : scalar, or arraylike of length equal to the number of columns
    in `dataframe`
        The labels to put on the columns. If scalar, it is broadcast into a
        list of length equal to the number of columns in `dataframe`.

    name_of_new_level : str
        The label to give the new level.

    inplace : bool, optional, default: False
        Whether to modify `dataframe` in place or to return a copy
        that is modified.

    Returns
    -------
    dataframe_with_new_columns : pandas DataFrame with MultiIndex columns
        The original `dataframe` with new columns that have the given `level`
        appended to each column label.
    """
    old_columns = dataframe.columns

    if not hasattr(new_level, '__len__') or isinstance(new_level, str):
        new_level = [new_level] * dataframe.shape[1]

    if isinstance(dataframe.columns, pd.MultiIndex):
        new_columns = pd.MultiIndex.from_arrays(
            old_columns.levels + [new_level],
            names=(old_columns.names + [name_of_new_level]))
    elif isinstance(dataframe.columns, pd.Index):
        new_columns = pd.MultiIndex.from_arrays(
            [old_columns] + [new_level],
            names=([old_columns.name] + [name_of_new_level]))

    if inplace:
        dataframe.columns = new_columns
        return dataframe
    else:
        copy_dataframe = dataframe.copy()
        copy_dataframe.columns = new_columns
        return copy_dataframe

Update: I learned from this solution another way to put a new level in a column, which makes it unnecessary to use append_level_to_columns_of_dataframe:

def shift_timeseries_by_lags_v2(df, lags, lag_label='lag'):
    return pd.concat({
        '{lag_label}_{lag_number}'.format(lag_label=lag_label, lag_number=lag):
        df.shift(lag)
        for lag in lags},
        axis=1)

Here's the result of shift_timeseries_by_lags_v2(pd.DataFrame(s), [0, 1, 2]):

Here is a cool one liner for lagged features with _lagN suffixes in column names using pd.concat:

lagged = pd.concat([s.shift(lag).rename('{}_lag{}'.format(s.name, lag+1)) for lag in range(3)], axis=1).dropna()

You can do following:

s=pd.Series([5,4,3,2,1], index=[1,2,3,4,5])
res = pd.DataFrame(index = s.index)
for l in range(3):
    res[l] = s.shift(l)
print res.ix[3:,:].as_matrix()

It produces:

array([[ 3.,  4.,  5.],
       [ 2.,  3.,  4.],
       [ 1.,  2.,  3.]])

which I hope is very close to what you are actually want.

For multiple (many of them) lags, this could be more compact:

df=pd.DataFrame({'year': range(2000, 2010), 'gdp': [234, 253, 256, 267, 272, 273, 271, 275, 280, 282]})
df.join(pd.DataFrame({'gdp_' + str(lag): df['gdp'].shift(lag) for lag in range(1,4)}))

Assuming you are focusing on a single column in your data frame, saved into s. This shortcode will generate instances of the column with 7 lags.

s=pd.Series([5,4,3,2,1], index=[1,2,3,4,5], name='test')
shiftdf=pd.DataFrame()
for i in range(3):
    shiftdf = pd.concat([shiftdf , s.shift(i).rename(s.name+'_'+str(i))], axis=1)

shiftdf

>>
test_0	test_1	test_2
1	5	NaN	NaN
2	4	5.0	NaN
3	3	4.0	5.0
4	2	3.0	4.0
5	1	2.0	3.0

Based on the proposal by @charlie-brummitt, here is a revision that fix a set of columns:

def shift_timeseries_by_lags(df, fix_columns, lag_numbers, lag_label='lag'):
    df_fix = df[fix_columns]
    df_lag = df.drop(columns=fix_columns)

    df_lagged = pd.concat({f'{lag_label}_{lag}':
        df_lag.shift(lag) for lag in lag_numbers},
        axis=1)
    df_lagged.columns = ['__'.join(reversed(x)) for x in df_lagged.columns.to_flat_index()]

    return pd.concat([df_fix, df_lagged], axis=1)

Here is an example of usage:

df = shift_timeseries_by_lags(df_province_cases, fix_columns=['country', 'state'], lag_numbers=[1,2,3])

I personally prefer the lag name as suffix. But can be changed removing reversed().