rouping
By “group by” we are referring to a process involving one or more of the following steps:
- Splitting the data into groups based on some criteria
- Applying a function to each group independently
- Combining the results into a data structure
See the Grouping section.
In [87]: df = pd.DataFrame(
....: {
....: "A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
....: "B": ["one", "one", "two", "three", "two", "two", "one", "three"],
....: "C": np.random.randn(8),
....: "D": np.random.randn(8),
....: }
....: )
....:
In [88]: df
Out[88]:
A B C D
0 foo one 1.346061 -1.577585
1 bar one 1.511763 0.396823
2 foo two 1.627081 -0.105381
3 bar three -0.990582 -0.532532
4 foo two -0.441652 1.453749
5 bar two 1.211526 1.208843
6 foo one 0.268520 -0.080952
7 foo three 0.024580 -0.264610Grouping and then applying the sum() function to the resulting groups:
In [89]: df.groupby("A")[["C", "D"]].sum()
Out[89]:
C D
A
bar 1.732707 1.073134
foo 2.824590 -0.574779Grouping by multiple columns forms a hierarchical index, and again we can apply the sum() function:
In [90]: df.groupby(["A", "B"]).sum()
Out[90]:
C D
A B
bar one 1.511763 0.396823
three -0.990582 -0.532532
two 1.211526 1.208843
foo one 1.614581 -1.658537
three 0.024580 -0.264610
two 1.185429 1.348368Reshaping
See the sections on Hierarchical Indexing and Reshaping.
Stack
In [91]: tuples = list(
....: zip(
....: ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"],
....: ["one", "two", "one", "two", "one", "two", "one", "two"],
....: )
....: )
....:
In [92]: index = pd.MultiIndex.from_tuples(tuples, names=["first", "second"])
In [93]: df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=["A", "B"])
In [94]: df2 = df[:4]
In [95]: df2
Out[95]:
A B
first second
bar one -0.727965 -0.589346
two 0.339969 -0.693205
baz one -0.339355 0.593616
two 0.884345 1.591431The stack() method “compresses” a level in the DataFrame’s columns:
In [96]: stacked = df2.stack()
In [97]: stacked
Out[97]:
first second
bar one A -0.727965
B -0.589346
two A 0.339969
B -0.693205
baz one A -0.339355
B 0.593616
two A 0.884345
B 1.591431
dtype: float64With a “stacked” DataFrame or Series (having a MultiIndex as the index), the inverse operation of stack() is unstack(), which by default unstacks the last level:
In [98]: stacked.unstack()
Out[98]:
A B
first second
bar one -0.727965 -0.589346
two 0.339969 -0.693205
baz one -0.339355 0.593616
two 0.884345 1.591431
In [99]: stacked.unstack(1)
Out[99]:
second one two
first
bar A -0.727965 0.339969
B -0.589346 -0.693205
baz A -0.339355 0.884345
B 0.593616 1.591431
In [100]: stacked.unstack(0)
Out[100]:
first bar baz
second
one A -0.727965 -0.339355
B -0.589346 0.593616
two A 0.339969 0.884345
B -0.693205 1.591431Pivot tables
See the section on Pivot Tables.
In [101]: df = pd.DataFrame(
.....: {
.....: "A": ["one", "one", "two", "three"] * 3,
.....: "B": ["A", "B", "C"] * 4,
.....: "C": ["foo", "foo", "foo", "bar", "bar", "bar"] * 2,
.....: "D": np.random.randn(12),
.....: "E": np.random.randn(12),
.....: }
.....: )
.....:
In [102]: df
Out[102]:
A B C D E
0 one A foo -1.202872 0.047609
1 one B foo -1.814470 -0.136473
2 two C foo 1.018601 -0.561757
3 three A bar -0.595447 -1.623033
4 one B bar 1.395433 0.029399
5 one C bar -0.392670 -0.542108
6 two A foo 0.007207 0.282696
7 three B foo 1.928123 -0.087302
8 one C foo -0.055224 -1.575170
9 one A bar 2.395985 1.771208
10 two B bar 1.552825 0.816482
11 three C bar 0.166599 1.100230pivot_table() pivots a DataFrame specifying the values, index and columns
In [103]: pd.pivot_table(df, values="D", index=["A", "B"], columns=["C"])
Out[103]:
C bar foo
A B
one A 2.395985 -1.202872
B 1.395433 -1.814470
C -0.392670 -0.055224
three A -0.595447 NaN
B NaN 1.928123
C 0.166599 NaN
two A NaN 0.007207
B 1.552825 NaN
C NaN 1.018601Time series
pandas has simple, powerful, and efficient functionality for performing resampling operations during frequency conversion (e.g., converting secondly data into 5-minutely data). This is extremely common in, but not limited to, financial applications. See the Time Series section.
In [104]: rng = pd.date_range("1/1/2012", periods=100, freq="S")
In [105]: ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)
In [106]: ts.resample("5Min").sum()
Out[106]:
2012-01-01 24182
Freq: 5T, dtype: int64Series.tz_localize() localizes a time series to a time zone:
In [107]: rng = pd.date_range("3/6/2012 00:00", periods=5, freq="D")
In [108]: ts = pd.Series(np.random.randn(len(rng)), rng)
In [109]: ts
Out[109]:
2012-03-06 1.857704
2012-03-07 -1.193545
2012-03-08 0.677510
2012-03-09 -0.153931
2012-03-10 0.520091
Freq: D, dtype: float64
In [110]: ts_utc = ts.tz_localize("UTC")
In [111]: ts_utc
Out[111]:
2012-03-06 00:00:00+00:00 1.857704
2012-03-07 00:00:00+00:00 -1.193545
2012-03-08 00:00:00+00:00 0.677510
2012-03-09 00:00:00+00:00 -0.153931
2012-03-10 00:00:00+00:00 0.520091
Freq: D, dtype: float64Series.tz_convert() converts a timezones aware time series to another time zone:
In [112]: ts_utc.tz_convert("US/Eastern")
Out[112]:
2012-03-05 19:00:00-05:00 1.857704
2012-03-06 19:00:00-05:00 -1.193545
2012-03-07 19:00:00-05:00 0.677510
2012-03-08 19:00:00-05:00 -0.153931
2012-03-09 19:00:00-05:00 0.520091
Freq: D, dtype: float64Converting between time span representations:
In [113]: rng = pd.date_range("1/1/2012", periods=5, freq="M")
In [114]: ts = pd.Series(np.random.randn(len(rng)), index=rng)
In [115]: ts
Out[115]:
2012-01-31 -1.475051
2012-02-29 0.722570
2012-03-31 -0.322646
2012-04-30 -1.601631
2012-05-31 0.778033
Freq: M, dtype: float64
In [116]: ps = ts.to_period()
In [117]: ps
Out[117]:
2012-01 -1.475051
2012-02 0.722570
2012-03 -0.322646
2012-04 -1.601631
2012-05 0.778033
Freq: M, dtype: float64
In [118]: ps.to_timestamp()
Out[118]:
2012-01-01 -1.475051
2012-02-01 0.722570
2012-03-01 -0.322646
2012-04-01 -1.601631
2012-05-01 0.778033
Freq: MS, dtype: float64Converting between period and timestamp enables some convenient arithmetic functions to be used. In the following example, we convert a quarterly frequency with year ending in November to 9am of the end of the month following the quarter end:
In [119]: prng = pd.period_range("1990Q1", "2000Q4", freq="Q-NOV")
In [120]: ts = pd.Series(np.random.randn(len(prng)), prng)
In [121]: ts.index = (prng.asfreq("M", "e") + 1).asfreq("H", "s") + 9
In [122]: ts.head()
Out[122]:
1990-03-01 09:00 -0.289342
1990-06-01 09:00 0.233141
1990-09-01 09:00 -0.223540
1990-12-01 09:00 0.542054
1991-03-01 09:00 -0.688585
Freq: H, dtype: float64