Essential Basic Functionality¶
Here we discuss a lot of the essential functionality common to the pandas data structures. Here’s how to create some of the objects used in the examples from the previous section:
In [1]: index = date_range('1/1/2000', periods=8)
In [2]: s = Series(randn(5), index=['a', 'b', 'c', 'd', 'e'])
In [3]: df = DataFrame(randn(8, 3), index=index,
...: columns=['A', 'B', 'C'])
...:
In [4]: wp = Panel(randn(2, 5, 4), items=['Item1', 'Item2'],
...: major_axis=date_range('1/1/2000', periods=5),
...: minor_axis=['A', 'B', 'C', 'D'])
...:
Head and Tail¶
To view a small sample of a Series or DataFrame object, use the head and tail methods. The default number of elements to display is five, but you may pass a custom number.
In [5]: long_series = Series(randn(1000))
In [6]: long_series.head()
Out[6]:
0 -0.199038
1 1.095864
2 -0.200875
3 0.162291
4 -0.430489
dtype: float64
In [7]: long_series.tail(3)
Out[7]:
997 -1.198693
998 1.238029
999 -1.344716
dtype: float64
Attributes and the raw ndarray(s)¶
pandas objects have a number of attributes enabling you to access the metadata
- shape: gives the axis dimensions of the object, consistent with ndarray
- Axis labels
- Series: index (only axis)
- DataFrame: index (rows) and columns
- Panel: items, major_axis, and minor_axis
Note, these attributes can be safely assigned to!
In [8]: df[:2]
Out[8]:
A B C
2000-01-01 0.232465 -0.789552 -0.364308
2000-01-02 -0.534541 0.822239 -0.443109
In [9]: df.columns = [x.lower() for x in df.columns]
In [10]: df
Out[10]:
a b c
2000-01-01 0.232465 -0.789552 -0.364308
2000-01-02 -0.534541 0.822239 -0.443109
2000-01-03 -2.119990 -0.460149 1.813962
2000-01-04 -1.053571 0.009412 -0.165966
2000-01-05 -0.848662 -0.495553 -0.176421
2000-01-06 -0.423595 -1.035433 -1.035374
2000-01-07 -2.369079 0.524408 -0.871120
2000-01-08 1.585433 0.039501 2.274101
To get the actual data inside a data structure, one need only access the values property:
In [11]: s.values
Out[11]: array([ 1.1292, 0.2313, -0.1847, -0.1386, -0.9243])
In [12]: df.values
Out[12]:
array([[ 0.2325, -0.7896, -0.3643],
[-0.5345, 0.8222, -0.4431],
[-2.12 , -0.4601, 1.814 ],
[-1.0536, 0.0094, -0.166 ],
[-0.8487, -0.4956, -0.1764],
[-0.4236, -1.0354, -1.0354],
[-2.3691, 0.5244, -0.8711],
[ 1.5854, 0.0395, 2.2741]])
In [13]: wp.values
Out[13]:
array([[[-1.1181, 0.4313, 0.5547, -1.3336],
[-0.3322, -0.4859, 1.7259, 1.7993],
[-0.9689, -0.7795, -2.0007, -1.8666],
[-1.1013, 1.9575, 0.0589, 0.7581],
[ 0.0766, -0.5485, -0.1605, -0.3778]],
[[ 0.2499, -0.3413, -0.2726, -0.2774],
[-1.1029, 0.1003, -1.6028, 0.9201],
[-0.6439, 0.0603, -0.4349, -0.4943],
[ 0.738 , 0.4516, 0.3341, -0.7871],
[ 0.6514, -0.7419, 1.1939, -2.3958]]])
If a DataFrame or Panel contains homogeneously-typed data, the ndarray can actually be modified in-place, and the changes will be reflected in the data structure. For heterogeneous data (e.g. some of the DataFrame’s columns are not all the same dtype), this will not be the case. The values attribute itself, unlike the axis labels, cannot be assigned to.
Note
When working with heterogeneous data, the dtype of the resulting ndarray will be chosen to accommodate all of the data involved. For example, if strings are involved, the result will be of object dtype. If there are only floats and integers, the resulting array will be of float dtype.
Accelerated operations¶
pandas has support for accelerating certain types of binary numerical and boolean operations using the numexpr library (starting in 0.11.0) and the bottleneck libraries.
These libraries are especially useful when dealing with large data sets, and provide large speedups. numexpr uses smart chunking, caching, and multiple cores. bottleneck is a set of specialized cython routines that are especially fast when dealing with arrays that have nans.
Here is a sample (using 100 column x 100,000 row DataFrames):
| Operation | 0.11.0 (ms) | Prior Version (ms) | Ratio to Prior |
|---|---|---|---|
| df1 > df2 | 13.32 | 125.35 | 0.1063 |
| df1 * df2 | 21.71 | 36.63 | 0.5928 |
| df1 + df2 | 22.04 | 36.50 | 0.6039 |
You are highly encouraged to install both libraries. See the section Recommended Dependencies for more installation info.
Flexible binary operations¶
With binary operations between pandas data structures, there are two key points of interest:
- Broadcasting behavior between higher- (e.g. DataFrame) and lower-dimensional (e.g. Series) objects.
- Missing data in computations
We will demonstrate how to manage these issues independently, though they can be handled simultaneously.
Matching / broadcasting behavior¶
DataFrame has the methods add, sub, mul, div and related functions radd, rsub, ... for carrying out binary operations. For broadcasting behavior, Series input is of primary interest. Using these functions, you can use to either match on the index or columns via the axis keyword:
In [14]: df = DataFrame({'one' : Series(randn(3), index=['a', 'b', 'c']),
....: 'two' : Series(randn(4), index=['a', 'b', 'c', 'd']),
....: 'three' : Series(randn(3), index=['b', 'c', 'd'])})
....:
In [15]: df
Out[15]:
one three two
a -0.701368 NaN -0.087103
b 0.109333 -0.354359 0.637674
c -0.231617 -0.148387 -0.002666
d NaN -0.167407 0.104044
In [16]: row = df.ix[1]
In [17]: column = df['two']
In [18]: df.sub(row, axis='columns')
Out[18]:
one three two
a -0.810701 NaN -0.724777
b 0.000000 0.000000 0.000000
c -0.340950 0.205973 -0.640340
d NaN 0.186952 -0.533630
In [19]: df.sub(row, axis=1)
Out[19]:
one three two
a -0.810701 NaN -0.724777
b 0.000000 0.000000 0.000000
c -0.340950 0.205973 -0.640340
d NaN 0.186952 -0.533630
In [20]: df.sub(column, axis='index')
Out[20]:
one three two
a -0.614265 NaN 0
b -0.528341 -0.992033 0
c -0.228950 -0.145720 0
d NaN -0.271451 0
In [21]: df.sub(column, axis=0)
Out[21]:
one three two
a -0.614265 NaN 0
b -0.528341 -0.992033 0
c -0.228950 -0.145720 0
d NaN -0.271451 0
Furthermore you can align a level of a multi-indexed DataFrame with a Series.
In [22]: dfmi = df.copy()
In [23]: dfmi.index = MultiIndex.from_tuples([(1,'a'),(1,'b'),(1,'c'),(2,'a')],
....: names=['first','second'])
....:
In [24]: dfmi.sub(column, axis=0, level='second')
Out[24]:
one three two
first second
1 a -0.614265 NaN 0.000000
b -0.528341 -0.992033 0.000000
c -0.228950 -0.145720 0.000000
2 a NaN -0.080304 0.191147
With Panel, describing the matching behavior is a bit more difficult, so the arithmetic methods instead (and perhaps confusingly?) give you the option to specify the broadcast axis. For example, suppose we wished to demean the data over a particular axis. This can be accomplished by taking the mean over an axis and broadcasting over the same axis:
In [25]: major_mean = wp.mean(axis='major')
In [26]: major_mean
Out[26]:
Item1 Item2
A -0.688773 -0.021497
B 0.114982 -0.094183
C 0.035674 -0.156470
D -0.204142 -0.606887
In [27]: wp.sub(major_mean, axis='major')
Out[27]:
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 5 (major_axis) x 4 (minor_axis)
Items axis: Item1 to Item2
Major_axis axis: 2000-01-01 00:00:00 to 2000-01-05 00:00:00
Minor_axis axis: A to D
And similarly for axis="items" and axis="minor".
Note
I could be convinced to make the axis argument in the DataFrame methods match the broadcasting behavior of Panel. Though it would require a transition period so users can change their code...
Missing data / operations with fill values¶
In Series and DataFrame (though not yet in Panel), the arithmetic functions have the option of inputting a fill_value, namely a value to substitute when at most one of the values at a location are missing. For example, when adding two DataFrame objects, you may wish to treat NaN as 0 unless both DataFrames are missing that value, in which case the result will be NaN (you can later replace NaN with some other value using fillna if you wish).
In [28]: df
Out[28]:
one three two
a -0.701368 NaN -0.087103
b 0.109333 -0.354359 0.637674
c -0.231617 -0.148387 -0.002666
d NaN -0.167407 0.104044
In [29]: df2
Out[29]:
one three two
a -0.701368 1.000000 -0.087103
b 0.109333 -0.354359 0.637674
c -0.231617 -0.148387 -0.002666
d NaN -0.167407 0.104044
In [30]: df + df2
Out[30]:
one three two
a -1.402736 NaN -0.174206
b 0.218666 -0.708719 1.275347
c -0.463233 -0.296773 -0.005333
d NaN -0.334814 0.208088
In [31]: df.add(df2, fill_value=0)
Out[31]:
one three two
a -1.402736 1.000000 -0.174206
b 0.218666 -0.708719 1.275347
c -0.463233 -0.296773 -0.005333
d NaN -0.334814 0.208088
Flexible Comparisons¶
Starting in v0.8, pandas introduced binary comparison methods eq, ne, lt, gt, le, and ge to Series and DataFrame whose behavior is analogous to the binary arithmetic operations described above:
In [32]: df.gt(df2)
Out[32]:
one three two
a False False False
b False False False
c False False False
d False False False
In [33]: df2.ne(df)
Out[33]:
one three two
a False True False
b False False False
c False False False
d True False False
These operations produce a pandas object the same type as the left-hand-side input that if of dtype bool. These boolean objects can be used in indexing operations, see here
Boolean Reductions¶
You can apply the reductions: empty, any(), all(), and bool() to provide a way to summarize a boolean result.
In [34]: (df>0).all()
Out[34]:
one False
three False
two False
dtype: bool
In [35]: (df>0).any()
Out[35]:
one True
three False
two True
dtype: bool
You can reduce to a final boolean value.
In [36]: (df>0).any().any()
Out[36]: True
You can test if a pandas object is empty, via the empty property.
In [37]: df.empty
Out[37]: False
In [38]: DataFrame(columns=list('ABC')).empty
Out[38]: True
To evaluate single-element pandas objects in a boolean context, use the method .bool():
In [39]: Series([True]).bool()
Out[39]: True
In [40]: Series([False]).bool()
Out[40]: False
In [41]: DataFrame([[True]]).bool()
Out[41]: True
In [42]: DataFrame([[False]]).bool()
Out[42]: False
Warning
You might be tempted to do the following:
>>>if df:
...
Or
>>> df and df2
These both will raise as you are trying to compare multiple values.
ValueError: The truth value of an array is ambiguous. Use a.empty, a.any() or a.all().
See gotchas for a more detailed discussion.
Comparing if objects are equivalent¶
Often you may find there is more than one way to compute the same result. As a simple example, consider df+df and df*2. To test that these two computations produce the same result, given the tools shown above, you might imagine using (df+df == df*2).all(). But in fact, this expression is False:
In [43]: df+df == df*2
Out[43]:
one three two
a True False True
b True True True
c True True True
d False True True
In [44]: (df+df == df*2).all()
Out[44]:
one False
three False
two True
dtype: bool
Notice that the boolean DataFrame df+df == df*2 contains some False values! That is because NaNs do not compare as equals:
In [45]: np.nan == np.nan
Out[45]: False
So, as of v0.13.1, NDFrames (such as Series, DataFrames, and Panels) have an equals method for testing equality, with NaNs in corresponding locations treated as equal.
In [46]: (df+df).equals(df*2)
Out[46]: True
Combining overlapping data sets¶
A problem occasionally arising is the combination of two similar data sets where values in one are preferred over the other. An example would be two data series representing a particular economic indicator where one is considered to be of “higher quality”. However, the lower quality series might extend further back in history or have more complete data coverage. As such, we would like to combine two DataFrame objects where missing values in one DataFrame are conditionally filled with like-labeled values from the other DataFrame. The function implementing this operation is combine_first, which we illustrate:
In [47]: df1 = DataFrame({'A' : [1., np.nan, 3., 5., np.nan],
....: 'B' : [np.nan, 2., 3., np.nan, 6.]})
....:
In [48]: df2 = DataFrame({'A' : [5., 2., 4., np.nan, 3., 7.],
....: 'B' : [np.nan, np.nan, 3., 4., 6., 8.]})
....:
In [49]: df1
Out[49]:
A B
0 1 NaN
1 NaN 2
2 3 3
3 5 NaN
4 NaN 6
In [50]: df2
Out[50]:
A B
0 5 NaN
1 2 NaN
2 4 3
3 NaN 4
4 3 6
5 7 8
In [51]: df1.combine_first(df2)
Out[51]:
A B
0 1 NaN
1 2 2
2 3 3
3 5 4
4 3 6
5 7 8
General DataFrame Combine¶
The combine_first method above calls the more general DataFrame method combine. This method takes another DataFrame and a combiner function, aligns the input DataFrame and then passes the combiner function pairs of Series (ie, columns whose names are the same).
So, for instance, to reproduce combine_first as above:
In [52]: combiner = lambda x, y: np.where(isnull(x), y, x)
In [53]: df1.combine(df2, combiner)
Out[53]:
A B
0 1 NaN
1 2 2
2 3 3
3 5 4
4 3 6
5 7 8
Descriptive statistics¶
A large number of methods for computing descriptive statistics and other related operations on Series, DataFrame, and Panel. Most of these are aggregations (hence producing a lower-dimensional result) like sum, mean, and quantile, but some of them, like cumsum and cumprod, produce an object of the same size. Generally speaking, these methods take an axis argument, just like ndarray.{sum, std, ...}, but the axis can be specified by name or integer:
- Series: no axis argument needed
- DataFrame: “index” (axis=0, default), “columns” (axis=1)
- Panel: “items” (axis=0), “major” (axis=1, default), “minor” (axis=2)
For example:
In [54]: df
Out[54]:
one three two
a -0.701368 NaN -0.087103
b 0.109333 -0.354359 0.637674
c -0.231617 -0.148387 -0.002666
d NaN -0.167407 0.104044
In [55]: df.mean(0)
Out[55]:
one -0.274551
three -0.223384
two 0.162987
dtype: float64
In [56]: df.mean(1)
Out[56]:
a -0.394235
b 0.130882
c -0.127557
d -0.031682
dtype: float64
All such methods have a skipna option signaling whether to exclude missing data (True by default):
In [57]: df.sum(0, skipna=False)
Out[57]:
one NaN
three NaN
two 0.651948
dtype: float64
In [58]: df.sum(axis=1, skipna=True)
Out[58]:
a -0.788471
b 0.392647
c -0.382670
d -0.063363
dtype: float64
Combined with the broadcasting / arithmetic behavior, one can describe various statistical procedures, like standardization (rendering data zero mean and standard deviation 1), very concisely:
In [59]: ts_stand = (df - df.mean()) / df.std()
In [60]: ts_stand.std()
Out[60]:
one 1
three 1
two 1
dtype: float64
In [61]: xs_stand = df.sub(df.mean(1), axis=0).div(df.std(1), axis=0)
In [62]: xs_stand.std(1)
Out[62]:
a 1
b 1
c 1
d 1
dtype: float64
Note that methods like cumsum and cumprod preserve the location of NA values:
In [63]: df.cumsum()
Out[63]:
one three two
a -0.701368 NaN -0.087103
b -0.592035 -0.354359 0.550570
c -0.823652 -0.502746 0.547904
d NaN -0.670153 0.651948
Here is a quick reference summary table of common functions. Each also takes an optional level parameter which applies only if the object has a hierarchical index.
| Function | Description |
|---|---|
| count | Number of non-null observations |
| sum | Sum of values |
| mean | Mean of values |
| mad | Mean absolute deviation |
| median | Arithmetic median of values |
| min | Minimum |
| max | Maximum |
| mode | Mode |
| abs | Absolute Value |
| prod | Product of values |
| std | Unbiased standard deviation |
| var | Unbiased variance |
| sem | Unbiased standard error of the mean |
| skew | Unbiased skewness (3rd moment) |
| kurt | Unbiased kurtosis (4th moment) |
| quantile | Sample quantile (value at %) |
| cumsum | Cumulative sum |
| cumprod | Cumulative product |
| cummax | Cumulative maximum |
| cummin | Cumulative minimum |
Note that by chance some NumPy methods, like mean, std, and sum, will exclude NAs on Series input by default:
In [64]: np.mean(df['one'])
Out[64]: -0.27455055654271204
In [65]: np.mean(df['one'].values)
Out[65]: nan
Series also has a method nunique which will return the number of unique non-null values:
In [66]: series = Series(randn(500))
In [67]: series[20:500] = np.nan
In [68]: series[10:20] = 5
In [69]: series.nunique()
Out[69]: 11
Summarizing data: describe¶
There is a convenient describe function which computes a variety of summary statistics about a Series or the columns of a DataFrame (excluding NAs of course):
In [70]: series = Series(randn(1000))
In [71]: series[::2] = np.nan
In [72]: series.describe()
Out[72]:
count 500.000000
mean -0.019898
std 1.019180
min -2.628792
25% -0.649795
50% -0.059405
75% 0.651932
max 3.240991
dtype: float64
In [73]: frame = DataFrame(randn(1000, 5), columns=['a', 'b', 'c', 'd', 'e'])
In [74]: frame.ix[::2] = np.nan
In [75]: frame.describe()
Out[75]:
a b c d e
count 500.000000 500.000000 500.000000 500.000000 500.000000
mean 0.051388 0.053476 -0.035612 0.015388 0.057804
std 0.989217 0.995961 0.977047 0.968385 1.022528
min -3.224136 -2.606460 -2.762875 -2.961757 -2.829100
25% -0.657420 -0.597123 -0.688961 -0.695019 -0.738097
50% 0.042928 0.018837 -0.071830 -0.011326 0.073287
75% 0.702445 0.693542 0.600454 0.680924 0.807670
max 3.034008 3.104512 2.812028 2.623914 3.542846
You can select specific percentiles to include in the output:
In [76]: series.describe(percentiles=[.05, .25, .75, .95])
Out[76]:
count 500.000000
mean -0.019898
std 1.019180
min -2.628792
5% -1.670021
25% -0.649795
50% -0.059405
75% 0.651932
95% 1.584100
max 3.240991
dtype: float64
By default, the median is always included.
For a non-numerical Series object, describe will give a simple summary of the number of unique values and most frequently occurring values:
In [77]: s = Series(['a', 'a', 'b', 'b', 'a', 'a', np.nan, 'c', 'd', 'a'])
In [78]: s.describe()
Out[78]:
count 9
unique 4
top a
freq 5
dtype: object
There also is a utility function, value_range which takes a DataFrame and returns a series with the minimum/maximum values in the DataFrame.
Index of Min/Max Values¶
The idxmin and idxmax functions on Series and DataFrame compute the index labels with the minimum and maximum corresponding values:
In [79]: s1 = Series(randn(5))
In [80]: s1
Out[80]:
0 -0.574018
1 0.668292
2 0.303418
3 -1.190271
4 0.138399
dtype: float64
In [81]: s1.idxmin(), s1.idxmax()
Out[81]: (3, 1)
In [82]: df1 = DataFrame(randn(5,3), columns=['A','B','C'])
In [83]: df1
Out[83]:
A B C
0 -0.184355 -1.054354 -1.613138
1 -0.050807 -2.130168 -1.852271
2 0.455674 2.571061 -1.152538
3 -1.638940 -0.364831 -0.348520
4 0.202856 0.777088 -0.358316
In [84]: df1.idxmin(axis=0)
Out[84]:
A 3
B 1
C 1
dtype: int64
In [85]: df1.idxmax(axis=1)
Out[85]:
0 A
1 A
2 B
3 C
4 B
dtype: object
When there are multiple rows (or columns) matching the minimum or maximum value, idxmin and idxmax return the first matching index:
In [86]: df3 = DataFrame([2, 1, 1, 3, np.nan], columns=['A'], index=list('edcba'))
In [87]: df3
Out[87]:
A
e 2
d 1
c 1
b 3
a NaN
In [88]: df3['A'].idxmin()
Out[88]: 'd'
Note
idxmin and idxmax are called argmin and argmax in NumPy.
Value counts (histogramming) / Mode¶
The value_counts Series method and top-level function computes a histogram of a 1D array of values. It can also be used as a function on regular arrays:
In [89]: data = np.random.randint(0, 7, size=50)
In [90]: data
Out[90]:
array([4, 6, 6, 1, 2, 1, 0, 5, 3, 2, 4, 3, 1, 3, 5, 3, 0, 0, 4, 4, 6, 1, 0,
4, 3, 2, 1, 3, 1, 5, 6, 3, 1, 2, 4, 4, 3, 3, 2, 2, 2, 3, 2, 3, 0, 1,
2, 4, 5, 5])
In [91]: s = Series(data)
In [92]: s.value_counts()
Out[92]:
3 11
2 9
4 8
1 8
5 5
0 5
6 4
dtype: int64
In [93]: value_counts(data)
Out[93]:
3 11
2 9
4 8
1 8
5 5
0 5
6 4
dtype: int64
Similarly, you can get the most frequently occurring value(s) (the mode) of the values in a Series or DataFrame:
In [94]: s5 = Series([1, 1, 3, 3, 3, 5, 5, 7, 7, 7])
In [95]: s5.mode()
Out[95]:
0 3
1 7
dtype: int64
In [96]: df5 = DataFrame({"A": np.random.randint(0, 7, size=50),
....: "B": np.random.randint(-10, 15, size=50)})
....:
In [97]: df5.mode()
Out[97]:
A B
0 5 -4
1 6 NaN
Discretization and quantiling¶
Continuous values can be discretized using the cut (bins based on values) and qcut (bins based on sample quantiles) functions:
In [98]: arr = np.random.randn(20)
In [99]: factor = cut(arr, 4)
In [100]: factor
Out[100]:
(-0.886, -0.0912]
(-0.886, -0.0912]
(-0.886, -0.0912]
(1.493, 2.285]
(0.701, 1.493]
...
(-0.0912, 0.701]
(-0.886, -0.0912]
(0.701, 1.493]
(0.701, 1.493]
(-0.0912, 0.701]
(1.493, 2.285]
Levels (4): Index(['(-0.886, -0.0912]', '(-0.0912, 0.701]',
'(0.701, 1.493]', '(1.493, 2.285]'], dtype=object)
Length: 20
In [101]: factor = cut(arr, [-5, -1, 0, 1, 5])
In [102]: factor
Out[102]:
(-1, 0]
(-1, 0]
(-1, 0]
(1, 5]
(1, 5]
...
(0, 1]
(-1, 0]
(0, 1]
(0, 1]
(0, 1]
(1, 5]
Levels (4): Index(['(-5, -1]', '(-1, 0]', '(0, 1]', '(1, 5]'], dtype=object)
Length: 20
qcut computes sample quantiles. For example, we could slice up some normally distributed data into equal-size quartiles like so:
In [103]: arr = np.random.randn(30)
In [104]: factor = qcut(arr, [0, .25, .5, .75, 1])
In [105]: factor
Out[105]:
[-1.861, -0.487]
(0.0554, 0.658]
(0.658, 2.259]
[-1.861, -0.487]
(0.658, 2.259]
...
(0.0554, 0.658]
(0.0554, 0.658]
(0.658, 2.259]
[-1.861, -0.487]
(0.0554, 0.658]
(-0.487, 0.0554]
Levels (4): Index(['[-1.861, -0.487]', '(-0.487, 0.0554]',
'(0.0554, 0.658]', '(0.658, 2.259]'], dtype=object)
Length: 30
In [106]: value_counts(factor)
Out[106]:
(0.658, 2.259] 8
[-1.861, -0.487] 8
(0.0554, 0.658] 7
(-0.487, 0.0554] 7
dtype: int64
We can also pass infinite values to define the bins:
In [107]: arr = np.random.randn(20)
In [108]: factor = cut(arr, [-np.inf, 0, np.inf])
In [109]: factor
Out[109]:
(0, inf]
(0, inf]
(-inf, 0]
(0, inf]
(-inf, 0]
...
(-inf, 0]
(0, inf]
(0, inf]
(-inf, 0]
(0, inf]
(-inf, 0]
Levels (2): Index(['(-inf, 0]', '(0, inf]'], dtype=object)
Length: 20
Function application¶
Arbitrary functions can be applied along the axes of a DataFrame or Panel using the apply method, which, like the descriptive statistics methods, take an optional axis argument:
In [110]: df.apply(np.mean)
Out[110]:
one -0.274551
three -0.223384
two 0.162987
dtype: float64
In [111]: df.apply(np.mean, axis=1)
Out[111]:
a -0.394235
b 0.130882
c -0.127557
d -0.031682
dtype: float64
In [112]: df.apply(lambda x: x.max() - x.min())
Out[112]:
one 0.810701
three 0.205973
two 0.724777
dtype: float64
In [113]: df.apply(np.cumsum)
Out[113]:
one three two
a -0.701368 NaN -0.087103
b -0.592035 -0.354359 0.550570
c -0.823652 -0.502746 0.547904
d NaN -0.670153 0.651948
In [114]: df.apply(np.exp)
Out[114]:
one three two
a 0.495907 NaN 0.916583
b 1.115534 0.701623 1.892074
c 0.793250 0.862098 0.997337
d NaN 0.845855 1.109649
Depending on the return type of the function passed to apply, the result will either be of lower dimension or the same dimension.
apply combined with some cleverness can be used to answer many questions about a data set. For example, suppose we wanted to extract the date where the maximum value for each column occurred:
In [115]: tsdf = DataFrame(randn(1000, 3), columns=['A', 'B', 'C'],
.....: index=date_range('1/1/2000', periods=1000))
.....:
In [116]: tsdf.apply(lambda x: x.idxmax())
Out[116]:
A 2002-08-19
B 2000-11-30
C 2002-01-10
dtype: datetime64[ns]
You may also pass additional arguments and keyword arguments to the apply method. For instance, consider the following function you would like to apply:
def subtract_and_divide(x, sub, divide=1):
return (x - sub) / divide
You may then apply this function as follows:
df.apply(subtract_and_divide, args=(5,), divide=3)
Another useful feature is the ability to pass Series methods to carry out some Series operation on each column or row:
In [117]: tsdf
Out[117]:
A B C
2000-01-01 -1.226159 0.173875 -0.798063
2000-01-02 0.127076 0.141070 -2.186743
2000-01-03 -1.804229 0.879800 0.465165
2000-01-04 NaN NaN NaN
2000-01-05 NaN NaN NaN
2000-01-06 NaN NaN NaN
2000-01-07 NaN NaN NaN
2000-01-08 1.542261 0.524780 1.445690
2000-01-09 -1.104998 -0.470200 0.336180
2000-01-10 -0.947692 -0.262122 -0.423769
In [118]: tsdf.apply(Series.interpolate)
Out[118]:
A B C
2000-01-01 -1.226159 0.173875 -0.798063
2000-01-02 0.127076 0.141070 -2.186743
2000-01-03 -1.804229 0.879800 0.465165
2000-01-04 -1.134931 0.808796 0.661270
2000-01-05 -0.465633 0.737792 0.857375
2000-01-06 0.203665 0.666788 1.053480
2000-01-07 0.872963 0.595784 1.249585
2000-01-08 1.542261 0.524780 1.445690
2000-01-09 -1.104998 -0.470200 0.336180
2000-01-10 -0.947692 -0.262122 -0.423769
Finally, apply takes an argument raw which is False by default, which converts each row or column into a Series before applying the function. When set to True, the passed function will instead receive an ndarray object, which has positive performance implications if you do not need the indexing functionality.
See also
The section on GroupBy demonstrates related, flexible functionality for grouping by some criterion, applying, and combining the results into a Series, DataFrame, etc.
Applying elementwise Python functions¶
Since not all functions can be vectorized (accept NumPy arrays and return another array or value), the methods applymap on DataFrame and analogously map on Series accept any Python function taking a single value and returning a single value. For example:
In [119]: df4
Out[119]:
one three two
a -0.701368 NaN -0.087103
b 0.109333 -0.354359 0.637674
c -0.231617 -0.148387 -0.002666
d NaN -0.167407 0.104044
In [120]: f = lambda x: len(str(x))
In [121]: df4['one'].map(f)
Out[121]:
a 15
b 14
c 15
d 3
Name: one, dtype: int64
In [122]: df4.applymap(f)
Out[122]:
one three two
a 15 3 16
b 14 15 14
c 15 15 17
d 3 15 14
Series.map has an additional feature which is that it can be used to easily “link” or “map” values defined by a secondary series. This is closely related to merging/joining functionality:
In [123]: s = Series(['six', 'seven', 'six', 'seven', 'six'],
.....: index=['a', 'b', 'c', 'd', 'e'])
.....:
In [124]: t = Series({'six' : 6., 'seven' : 7.})
In [125]: s
Out[125]:
a six
b seven
c six
d seven
e six
dtype: object
In [126]: s.map(t)
Out[126]:
a 6
b 7
c 6
d 7
e 6
dtype: float64
Applying with a Panel¶
Applying with a Panel will pass a Series to the applied function. If the applied function returns a Series, the result of the application will be a Panel. If the applied function reduces to a scalar, the result of the application will be a DataFrame.
Note
Prior to 0.13.1 apply on a Panel would only work on ufuncs (e.g. np.sum/np.max).
In [127]: import pandas.util.testing as tm
In [128]: panel = tm.makePanel(5)
In [129]: panel
Out[129]:
<class 'pandas.core.panel.Panel'>
Dimensions: 3 (items) x 5 (major_axis) x 4 (minor_axis)
Items axis: ItemA to ItemC
Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
Minor_axis axis: A to D
In [130]: panel['ItemA']
Out[130]:
A B C D
2000-01-03 0.166882 -0.597361 -1.200639 0.174260
2000-01-04 -1.759496 -1.514940 -1.872993 -0.581163
2000-01-05 0.901336 -1.640398 0.825210 0.087916
2000-01-06 -0.317478 -1.130643 -0.392715 0.416971
2000-01-07 -0.681335 -0.245890 -1.994150 0.666084
A transformational apply.
In [131]: result = panel.apply(lambda x: x*2, axis='items')
In [132]: result
Out[132]:
<class 'pandas.core.panel.Panel'>
Dimensions: 3 (items) x 5 (major_axis) x 4 (minor_axis)
Items axis: ItemA to ItemC
Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
Minor_axis axis: A to D
In [133]: result['ItemA']
Out[133]:
A B C D
2000-01-03 0.333764 -1.194722 -2.401278 0.348520
2000-01-04 -3.518991 -3.029880 -3.745986 -1.162326
2000-01-05 1.802673 -3.280796 1.650421 0.175832
2000-01-06 -0.634955 -2.261286 -0.785430 0.833943
2000-01-07 -1.362670 -0.491779 -3.988300 1.332168
A reduction operation.
In [134]: panel.apply(lambda x: x.dtype, axis='items')
Out[134]:
A B C D
2000-01-03 float64 float64 float64 float64
2000-01-04 float64 float64 float64 float64
2000-01-05 float64 float64 float64 float64
2000-01-06 float64 float64 float64 float64
2000-01-07 float64 float64 float64 float64
A similar reduction type operation
In [135]: panel.apply(lambda x: x.sum(), axis='major_axis')
Out[135]:
ItemA ItemB ItemC
A -1.690090 1.840259 0.010754
B -5.129232 0.860182 0.178018
C -4.635286 0.545328 2.456520
D 0.764068 -3.623586 1.761541
This last reduction is equivalent to
In [136]: panel.sum('major_axis')
Out[136]:
ItemA ItemB ItemC
A -1.690090 1.840259 0.010754
B -5.129232 0.860182 0.178018
C -4.635286 0.545328 2.456520
D 0.764068 -3.623586 1.761541
A transformation operation that returns a Panel, but is computing the z-score across the major_axis.
In [137]: result = panel.apply(
.....: lambda x: (x-x.mean())/x.std(),
.....: axis='major_axis')
.....:
In [138]: result
Out[138]:
<class 'pandas.core.panel.Panel'>
Dimensions: 3 (items) x 5 (major_axis) x 4 (minor_axis)
Items axis: ItemA to ItemC
Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
Minor_axis axis: A to D
In [139]: result['ItemA']
Out[139]:
A B C D
2000-01-03 0.509389 0.719204 -0.234072 0.045812
2000-01-04 -1.434116 -0.820934 -0.809328 -1.567858
2000-01-05 1.250373 -1.031513 1.499214 -0.138629
2000-01-06 0.020723 -0.175899 0.457175 0.564271
2000-01-07 -0.346370 1.309142 -0.912988 1.096405
Apply can also accept multiple axes in the axis argument. This will pass a DataFrame of the cross-section to the applied function.
In [140]: f = lambda x: ((x.T-x.mean(1))/x.std(1)).T
In [141]: result = panel.apply(f, axis = ['items','major_axis'])
In [142]: result
Out[142]:
<class 'pandas.core.panel.Panel'>
Dimensions: 4 (items) x 5 (major_axis) x 3 (minor_axis)
Items axis: A to D
Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
Minor_axis axis: ItemA to ItemC
In [143]: result.loc[:,:,'ItemA']
Out[143]:
A B C D
2000-01-03 0.783778 -0.648605 -0.903128 0.450190
2000-01-04 -0.884670 -1.046087 -1.096521 -0.900467
2000-01-05 1.140729 -1.124651 0.716895 0.754324
2000-01-06 -1.043494 0.029043 -0.991042 0.845339
2000-01-07 -1.125870 -0.536928 -1.152240 -0.182526
This is equivalent to the following
In [144]: result = Panel(dict([ (ax,f(panel.loc[:,:,ax]))
.....: for ax in panel.minor_axis ]))
.....:
In [145]: result
Out[145]:
<class 'pandas.core.panel.Panel'>
Dimensions: 4 (items) x 5 (major_axis) x 3 (minor_axis)
Items axis: A to D
Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
Minor_axis axis: ItemA to ItemC
In [146]: result.loc[:,:,'ItemA']
Out[146]:
A B C D
2000-01-03 0.783778 -0.648605 -0.903128 0.450190
2000-01-04 -0.884670 -1.046087 -1.096521 -0.900467
2000-01-05 1.140729 -1.124651 0.716895 0.754324
2000-01-06 -1.043494 0.029043 -0.991042 0.845339
2000-01-07 -1.125870 -0.536928 -1.152240 -0.182526
Reindexing and altering labels¶
reindex is the fundamental data alignment method in pandas. It is used to implement nearly all other features relying on label-alignment functionality. To reindex means to conform the data to match a given set of labels along a particular axis. This accomplishes several things:
- Reorders the existing data to match a new set of labels
- Inserts missing value (NA) markers in label locations where no data for that label existed
- If specified, fill data for missing labels using logic (highly relevant to working with time series data)
Here is a simple example:
In [147]: s = Series(randn(5), index=['a', 'b', 'c', 'd', 'e'])
In [148]: s
Out[148]:
a 1.112686
b -1.069046
c -1.218080
d -0.944778
e 0.005240
dtype: float64
In [149]: s.reindex(['e', 'b', 'f', 'd'])
Out[149]:
e 0.005240
b -1.069046
f NaN
d -0.944778
dtype: float64
Here, the f label was not contained in the Series and hence appears as NaN in the result.
With a DataFrame, you can simultaneously reindex the index and columns:
In [150]: df
Out[150]:
one three two
a -0.701368 NaN -0.087103
b 0.109333 -0.354359 0.637674
c -0.231617 -0.148387 -0.002666
d NaN -0.167407 0.104044
In [151]: df.reindex(index=['c', 'f', 'b'], columns=['three', 'two', 'one'])
Out[151]:
three two one
c -0.148387 -0.002666 -0.231617
f NaN NaN NaN
b -0.354359 0.637674 0.109333
For convenience, you may utilize the reindex_axis method, which takes the labels and a keyword axis parameter.
Note that the Index objects containing the actual axis labels can be shared between objects. So if we have a Series and a DataFrame, the following can be done:
In [152]: rs = s.reindex(df.index)
In [153]: rs
Out[153]:
a 1.112686
b -1.069046
c -1.218080
d -0.944778
dtype: float64
In [154]: rs.index is df.index
Out[154]: True
This means that the reindexed Series’s index is the same Python object as the DataFrame’s index.
See also
Advanced indexing is an even more concise way of doing reindexing.
Note
When writing performance-sensitive code, there is a good reason to spend some time becoming a reindexing ninja: many operations are faster on pre-aligned data. Adding two unaligned DataFrames internally triggers a reindexing step. For exploratory analysis you will hardly notice the difference (because reindex has been heavily optimized), but when CPU cycles matter sprinkling a few explicit reindex calls here and there can have an impact.
Reindexing to align with another object¶
You may wish to take an object and reindex its axes to be labeled the same as another object. While the syntax for this is straightforward albeit verbose, it is a common enough operation that the reindex_like method is available to make this simpler:
In [155]: df2
Out[155]:
one two
a -0.701368 -0.087103
b 0.109333 0.637674
c -0.231617 -0.002666
In [156]: df3
Out[156]:
one two
a -0.426817 -0.269738
b 0.383883 0.455039
c 0.042934 -0.185301
In [157]: df.reindex_like(df2)
Out[157]:
one two
a -0.701368 -0.087103
b 0.109333 0.637674
c -0.231617 -0.002666
Reindexing with reindex_axis¶
Aligning objects with each other with align¶
The align method is the fastest way to simultaneously align two objects. It supports a join argument (related to joining and merging):
- join='outer': take the union of the indexes
- join='left': use the calling object’s index
- join='right': use the passed object’s index
- join='inner': intersect the indexes
It returns a tuple with both of the reindexed Series:
In [158]: s = Series(randn(5), index=['a', 'b', 'c', 'd', 'e'])
In [159]: s1 = s[:4]
In [160]: s2 = s[1:]
In [161]: s1.align(s2)
Out[161]:
(a 0.479090
b 0.686579
c -0.949750
d -0.257472
e NaN
dtype: float64, a NaN
b 0.686579
c -0.949750
d -0.257472
e -0.568459
dtype: float64)
In [162]: s1.align(s2, join='inner')
Out[162]:
(b 0.686579
c -0.949750
d -0.257472
dtype: float64, b 0.686579
c -0.949750
d -0.257472
dtype: float64)
In [163]: s1.align(s2, join='left')
Out[163]:
(a 0.479090
b 0.686579
c -0.949750
d -0.257472
dtype: float64, a NaN
b 0.686579
c -0.949750
d -0.257472
dtype: float64)
For DataFrames, the join method will be applied to both the index and the columns by default:
In [164]: df.align(df2, join='inner')
Out[164]:
( one two
a -0.701368 -0.087103
b 0.109333 0.637674
c -0.231617 -0.002666, one two
a -0.701368 -0.087103
b 0.109333 0.637674
c -0.231617 -0.002666)
You can also pass an axis option to only align on the specified axis:
In [165]: df.align(df2, join='inner', axis=0)
Out[165]:
( one three two
a -0.701368 NaN -0.087103
b 0.109333 -0.354359 0.637674
c -0.231617 -0.148387 -0.002666, one two
a -0.701368 -0.087103
b 0.109333 0.637674
c -0.231617 -0.002666)
If you pass a Series to DataFrame.align, you can choose to align both objects either on the DataFrame’s index or columns using the axis argument:
In [166]: df.align(df2.ix[0], axis=1)
Out[166]:
( one three two
a -0.701368 NaN -0.087103
b 0.109333 -0.354359 0.637674
c -0.231617 -0.148387 -0.002666
d NaN -0.167407 0.104044, one -0.701368
three NaN
two -0.087103
Name: a, dtype: float64)
Filling while reindexing¶
reindex takes an optional parameter method which is a filling method chosen from the following table:
| Method | Action |
|---|---|
| pad / ffill | Fill values forward |
| bfill / backfill | Fill values backward |
Other fill methods could be added, of course, but these are the two most commonly used for time series data. In a way they only make sense for time series or otherwise ordered data, but you may have an application on non-time series data where this sort of “interpolation” logic is the correct thing to do. More sophisticated interpolation of missing values would be an obvious extension.
We illustrate these fill methods on a simple TimeSeries:
In [167]: rng = date_range('1/3/2000', periods=8)
In [168]: ts = Series(randn(8), index=rng)
In [169]: ts2 = ts[[0, 3, 6]]
In [170]: ts
Out[170]:
2000-01-03 -0.059786
2000-01-04 0.936271
2000-01-05 0.040623
2000-01-06 0.836517
2000-01-07 1.849649
2000-01-08 -1.198994
2000-01-09 0.688500
2000-01-10 -0.696903
Freq: D, dtype: float64
In [171]: ts2
Out[171]:
2000-01-03 -0.059786
2000-01-06 0.836517
2000-01-09 0.688500
dtype: float64
In [172]: ts2.reindex(ts.index)
Out[172]:
2000-01-03 -0.059786
2000-01-04 NaN
2000-01-05 NaN
2000-01-06 0.836517
2000-01-07 NaN
2000-01-08 NaN
2000-01-09 0.688500
2000-01-10 NaN
Freq: D, dtype: float64
In [173]: ts2.reindex(ts.index, method='ffill')
Out[173]:
2000-01-03 -0.059786
2000-01-04 -0.059786
2000-01-05 -0.059786
2000-01-06 0.836517
2000-01-07 0.836517
2000-01-08 0.836517
2000-01-09 0.688500
2000-01-10 0.688500
Freq: D, dtype: float64
In [174]: ts2.reindex(ts.index, method='bfill')
Out[174]:
2000-01-03 -0.059786
2000-01-04 0.836517
2000-01-05 0.836517
2000-01-06 0.836517
2000-01-07 0.688500
2000-01-08 0.688500
2000-01-09 0.688500
2000-01-10 NaN
Freq: D, dtype: float64
Note these methods require that the indexes are order increasing.
Note the same result could have been achieved using fillna:
In [175]: ts2.reindex(ts.index).fillna(method='ffill')
Out[175]:
2000-01-03 -0.059786
2000-01-04 -0.059786
2000-01-05 -0.059786
2000-01-06 0.836517
2000-01-07 0.836517
2000-01-08 0.836517
2000-01-09 0.688500
2000-01-10 0.688500
Freq: D, dtype: float64
Note that reindex will raise a ValueError if the index is not monotonic. fillna will not make any checks on the order of the index.
Dropping labels from an axis¶
A method closely related to reindex is the drop function. It removes a set of labels from an axis:
In [176]: df
Out[176]:
one three two
a -0.701368 NaN -0.087103
b 0.109333 -0.354359 0.637674
c -0.231617 -0.148387 -0.002666
d NaN -0.167407 0.104044
In [177]: df.drop(['a', 'd'], axis=0)
Out[177]:
one three two
b 0.109333 -0.354359 0.637674
c -0.231617 -0.148387 -0.002666
In [178]: df.drop(['one'], axis=1)
Out[178]:
three two
a NaN -0.087103
b -0.354359 0.637674
c -0.148387 -0.002666
d -0.167407 0.104044
Note that the following also works, but is a bit less obvious / clean:
In [179]: df.reindex(df.index - ['a', 'd'])
Out[179]:
one three two
b 0.109333 -0.354359 0.637674
c -0.231617 -0.148387 -0.002666
Renaming / mapping labels¶
The rename method allows you to relabel an axis based on some mapping (a dict or Series) or an arbitrary function.
In [180]: s
Out[180]:
a 0.479090
b 0.686579
c -0.949750
d -0.257472
e -0.568459
dtype: float64
In [181]: s.rename(str.upper)
Out[181]:
A 0.479090
B 0.686579
C -0.949750
D -0.257472
E -0.568459
dtype: float64
If you pass a function, it must return a value when called with any of the labels (and must produce a set of unique values). But if you pass a dict or Series, it need only contain a subset of the labels as keys:
In [182]: df.rename(columns={'one' : 'foo', 'two' : 'bar'},
.....: index={'a' : 'apple', 'b' : 'banana', 'd' : 'durian'})
.....:
Out[182]:
foo three bar
apple -0.701368 NaN -0.087103
banana 0.109333 -0.354359 0.637674
c -0.231617 -0.148387 -0.002666
durian NaN -0.167407 0.104044
The rename method also provides an inplace named parameter that is by default False and copies the underlying data. Pass inplace=True to rename the data in place.
The Panel class has a related rename_axis class which can rename any of its three axes.
Iteration¶
Because Series is array-like, basic iteration produces the values. Other data structures follow the dict-like convention of iterating over the “keys” of the objects. In short:
- Series: values
- DataFrame: column labels
- Panel: item labels
Thus, for example:
In [183]: for col in df:
.....: print(col)
.....:
one
three
two
iteritems¶
Consistent with the dict-like interface, iteritems iterates through key-value pairs:
- Series: (index, scalar value) pairs
- DataFrame: (column, Series) pairs
- Panel: (item, DataFrame) pairs
For example:
In [184]: for item, frame in wp.iteritems():
.....: print(item)
.....: print(frame)
.....:
Item1
A B C D
2000-01-01 -1.118121 0.431279 0.554724 -1.333649
2000-01-02 -0.332174 -0.485882 1.725945 1.799276
2000-01-03 -0.968916 -0.779465 -2.000701 -1.866630
2000-01-04 -1.101268 1.957478 0.058889 0.758071
2000-01-05 0.076612 -0.548502 -0.160485 -0.377780
Item2
A B C D
2000-01-01 0.249911 -0.341270 -0.272599 -0.277446
2000-01-02 -1.102896 0.100307 -1.602814 0.920139
2000-01-03 -0.643870 0.060336 -0.434942 -0.494305
2000-01-04 0.737973 0.451632 0.334124 -0.787062
2000-01-05 0.651396 -0.741919 1.193881 -2.395763
iterrows¶
New in v0.7 is the ability to iterate efficiently through rows of a DataFrame. It returns an iterator yielding each index value along with a Series containing the data in each row:
In [185]: for row_index, row in df2.iterrows():
.....: print('%s\n%s' % (row_index, row))
.....:
a
one -0.701368
two -0.087103
Name: a, dtype: float64
b
one 0.109333
two 0.637674
Name: b, dtype: float64
c
one -0.231617
two -0.002666
Name: c, dtype: float64
For instance, a contrived way to transpose the DataFrame would be:
In [186]: df2 = DataFrame({'x': [1, 2, 3], 'y': [4, 5, 6]})
In [187]: print(df2)
x y
0 1 4
1 2 5
2 3 6
In [188]: print(df2.T)
0 1 2
x 1 2 3
y 4 5 6
In [189]: df2_t = DataFrame(dict((idx,values) for idx, values in df2.iterrows()))
In [190]: print(df2_t)
0 1 2
x 1 2 3
y 4 5 6
Note
iterrows does not preserve dtypes across the rows (dtypes are preserved across columns for DataFrames). For example,
In [191]: df_iter = DataFrame([[1, 1.0]], columns=['x', 'y']) In [192]: row = next(df_iter.iterrows())[1] In [193]: print(row['x'].dtype) float64 In [194]: print(df_iter['x'].dtype) int64
itertuples¶
This method will return an iterator yielding a tuple for each row in the DataFrame. The first element of the tuple will be the row’s corresponding index value, while the remaining values are the row values proper.
For instance,
In [195]: for r in df2.itertuples():
.....: print(r)
.....:
(0, 1, 4)
(1, 2, 5)
(2, 3, 6)
Vectorized string methods¶
Series is equipped (as of pandas 0.8.1) with a set of string processing methods that make it easy to operate on each element of the array. Perhaps most importantly, these methods exclude missing/NA values automatically. These are accessed via the Series’s str attribute and generally have names matching the equivalent (scalar) build-in string methods:
Splitting and Replacing Strings¶
In [196]: s = Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])
In [197]: s.str.lower()
Out[197]:
0 a
1 b
2 c
3 aaba
4 baca
5 NaN
6 caba
7 dog
8 cat
dtype: object
In [198]: s.str.upper()
Out[198]:
0 A
1 B
2 C
3 AABA
4 BACA
5 NaN
6 CABA
7 DOG
8 CAT
dtype: object
In [199]: s.str.len()
Out[199]:
0 1
1 1
2 1
3 4
4 4
5 NaN
6 4
7 3
8 3
dtype: float64
Methods like split return a Series of lists:
In [200]: s2 = Series(['a_b_c', 'c_d_e', np.nan, 'f_g_h'])
In [201]: s2.str.split('_')
Out[201]:
0 [a, b, c]
1 [c, d, e]
2 NaN
3 [f, g, h]
dtype: object
Elements in the split lists can be accessed using get or [] notation:
In [202]: s2.str.split('_').str.get(1)
Out[202]:
0 b
1 d
2 NaN
3 g
dtype: object
In [203]: s2.str.split('_').str[1]
Out[203]:
0 b
1 d
2 NaN
3 g
dtype: object
Methods like replace and findall take regular expressions, too:
In [204]: s3 = Series(['A', 'B', 'C', 'Aaba', 'Baca',
.....: '', np.nan, 'CABA', 'dog', 'cat'])
.....:
In [205]: s3
Out[205]:
0 A
1 B
2 C
3 Aaba
4 Baca
5
6 NaN
7 CABA
8 dog
9 cat
dtype: object
In [206]: s3.str.replace('^.a|dog', 'XX-XX ', case=False)
Out[206]:
0 A
1 B
2 C
3 XX-XX ba
4 XX-XX ca
5
6 NaN
7 XX-XX BA
8 XX-XX
9 XX-XX t
dtype: object
Extracting Substrings¶
The method extract (introduced in version 0.13) accepts regular expressions with match groups. Extracting a regular expression with one group returns a Series of strings.
In [207]: Series(['a1', 'b2', 'c3']).str.extract('[ab](\d)')
Out[207]:
0 1
1 2
2 NaN
dtype: object
Elements that do not match return NaN. Extracting a regular expression with more than one group returns a DataFrame with one column per group.
In [208]: Series(['a1', 'b2', 'c3']).str.extract('([ab])(\d)')
Out[208]:
0 1
0 a 1
1 b 2
2 NaN NaN
Elements that do not match return a row filled with NaN. Thus, a Series of messy strings can be “converted” into a like-indexed Series or DataFrame of cleaned-up or more useful strings, without necessitating get() to access tuples or re.match objects.
The results dtype always is object, even if no match is found and the result only contains NaN.
Named groups like
In [209]: Series(['a1', 'b2', 'c3']).str.extract('(?P<letter>[ab])(?P<digit>\d)')
Out[209]:
letter digit
0 a 1
1 b 2
2 NaN NaN
and optional groups like
In [210]: Series(['a1', 'b2', '3']).str.extract('(?P<letter>[ab])?(?P<digit>\d)')
Out[210]:
letter digit
0 a 1
1 b 2
2 NaN 3
can also be used.
Testing for Strings that Match or Contain a Pattern¶
You can check whether elements contain a pattern:
In [211]: pattern = r'[a-z][0-9]'
In [212]: Series(['1', '2', '3a', '3b', '03c']).str.contains(pattern)
Out[212]:
0 False
1 False
2 False
3 False
4 False
dtype: bool
or match a pattern:
In [213]: Series(['1', '2', '3a', '3b', '03c']).str.match(pattern, as_indexer=True)
Out[213]:
0 False
1 False
2 False
3 False
4 False
dtype: bool
The distinction between match and contains is strictness: match relies on strict re.match, while contains relies on re.search.
Warning
In previous versions, match was for extracting groups, returning a not-so-convenient Series of tuples. The new method extract (described in the previous section) is now preferred.
This old, deprecated behavior of match is still the default. As demonstrated above, use the new behavior by setting as_indexer=True. In this mode, match is analogous to contains, returning a boolean Series. The new behavior will become the default behavior in a future release.
- Methods like match, contains, startswith, and endswith take
- an extra na argument so missing values can be considered True or False:
In [214]: s4 = Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])
In [215]: s4.str.contains('A', na=False)
Out[215]:
0 True
1 False
2 False
3 True
4 False
5 False
6 True
7 False
8 False
dtype: bool
| Method | Description |
|---|---|
| cat | Concatenate strings |
| split | Split strings on delimiter |
| get | Index into each element (retrieve i-th element) |
| join | Join strings in each element of the Series with passed separator |
| contains | Return boolean array if each string contains pattern/regex |
| replace | Replace occurrences of pattern/regex with some other string |
| repeat | Duplicate values (s.str.repeat(3) equivalent to x * 3) |
| pad | Add whitespace to left, right, or both sides of strings |
| center | Equivalent to pad(side='both') |
| wrap | Split long strings into lines with length less than a given width |
| slice | Slice each string in the Series |
| slice_replace | Replace slice in each string with passed value |
| count | Count occurrences of pattern |
| startswith | Equivalent to str.startswith(pat) for each element |
| endswith | Equivalent to str.endswith(pat) for each element |
| findall | Compute list of all occurrences of pattern/regex for each string |
| match | Call re.match on each element, returning matched groups as list |
| extract | Call re.match on each element, as match does, but return matched groups as strings for convenience. |
| len | Compute string lengths |
| strip | Equivalent to str.strip |
| rstrip | Equivalent to str.rstrip |
| lstrip | Equivalent to str.lstrip |
| lower | Equivalent to str.lower |
| upper | Equivalent to str.upper |
Getting indicator variables from seperated strings¶
You can extract dummy variables from string columns. For example if they are seperated by a '|':
In [216]: s = pd.Series(['a', 'a|b', np.nan, 'a|c']) In [217]: s.str.get_dummies(sep='|') Out[217]: a b c 0 1 0 0 1 1 1 0 2 0 0 0 3 1 0 1
See also get_dummies().
Sorting by index and value¶
There are two obvious kinds of sorting that you may be interested in: sorting by label and sorting by actual values. The primary method for sorting axis labels (indexes) across data structures is the sort_index method.
In [218]: unsorted_df = df.reindex(index=['a', 'd', 'c', 'b'],
.....: columns=['three', 'two', 'one'])
.....:
In [219]: unsorted_df.sort_index()
Out[219]:
three two one
a NaN -0.087103 -0.701368
b -0.354359 0.637674 0.109333
c -0.148387 -0.002666 -0.231617
d -0.167407 0.104044 NaN
In [220]: unsorted_df.sort_index(ascending=False)
Out[220]:
three two one
d -0.167407 0.104044 NaN
c -0.148387 -0.002666 -0.231617
b -0.354359 0.637674 0.109333
a NaN -0.087103 -0.701368
In [221]: unsorted_df.sort_index(axis=1)
Out[221]:
one three two
a -0.701368 NaN -0.087103
d NaN -0.167407 0.104044
c -0.231617 -0.148387 -0.002666
b 0.109333 -0.354359 0.637674
DataFrame.sort_index can accept an optional by argument for axis=0 which will use an arbitrary vector or a column name of the DataFrame to determine the sort order:
In [222]: df1 = DataFrame({'one':[2,1,1,1],'two':[1,3,2,4],'three':[5,4,3,2]})
In [223]: df1.sort_index(by='two')
Out[223]:
one three two
0 2 5 1
2 1 3 2
1 1 4 3
3 1 2 4
The by argument can take a list of column names, e.g.:
In [224]: df1[['one', 'two', 'three']].sort_index(by=['one','two'])
Out[224]:
one two three
2 1 2 3
1 1 3 4
3 1 4 2
0 2 1 5
Series has the method order (analogous to R’s order function) which sorts by value, with special treatment of NA values via the na_position argument:
In [225]: s[2] = np.nan
In [226]: s.order()
Out[226]:
0 a
1 a|b
3 a|c
2 NaN
dtype: object
In [227]: s.order(na_position='first')
Out[227]:
2 NaN
0 a
1 a|b
3 a|c
dtype: object
Note
Series.sort sorts a Series by value in-place. This is to provide compatibility with NumPy methods which expect the ndarray.sort behavior. Series.order returns a copy of the sorted data.
smallest / largest values¶
New in version 0.14.0.
Series has the nsmallest and nlargest methods which return the
smallest or largest 
values. For a large Series this can be much
faster than sorting the entire Series and calling head(n) on the result.
In [228]: s = Series(np.random.permutation(10))
In [229]: s
Out[229]:
0 6
1 2
2 7
3 3
4 9
5 4
6 8
7 0
8 1
9 5
dtype: int32
In [230]: s.order()
Out[230]:
7 0
8 1
1 2
3 3
5 4
9 5
0 6
2 7
6 8
4 9
dtype: int32
In [231]: s.nsmallest(3)
Out[231]:
7 0
8 1
1 2
dtype: int32
In [232]: s.nlargest(3)
Out[232]:
4 9
6 8
2 7
dtype: int32
Sorting by a multi-index column¶
You must be explicit about sorting when the column is a multi-index, and fully specify all levels to by.
In [233]: df1.columns = MultiIndex.from_tuples([('a','one'),('a','two'),('b','three')])
In [234]: df1.sort_index(by=('a','two'))
Out[234]:
a b
one two three
3 1 2 4
2 1 3 2
1 1 4 3
0 2 5 1
Copying¶
The copy method on pandas objects copies the underlying data (though not the axis indexes, since they are immutable) and returns a new object. Note that it is seldom necessary to copy objects. For example, there are only a handful of ways to alter a DataFrame in-place:
- Inserting, deleting, or modifying a column
- Assigning to the index or columns attributes
- For homogeneous data, directly modifying the values via the values attribute or advanced indexing
To be clear, no pandas methods have the side effect of modifying your data; almost all methods return new objects, leaving the original object untouched. If data is modified, it is because you did so explicitly.
dtypes¶
The main types stored in pandas objects are float, int, bool, datetime64[ns], timedelta[ns], and object. In addition these dtypes have item sizes, e.g. int64 and int32. A convenient dtypes attribute for DataFrames returns a Series with the data type of each column.
In [235]: dft = DataFrame(dict( A = np.random.rand(3),
.....: B = 1,
.....: C = 'foo',
.....: D = Timestamp('20010102'),
.....: E = Series([1.0]*3).astype('float32'),
.....: F = False,
.....: G = Series([1]*3,dtype='int8')))
.....:
In [236]: dft
Out[236]:
A B C D E F G
0 0.193366 1 foo 2001-01-02 1 False 1
1 0.013428 1 foo 2001-01-02 1 False 1
2 0.347430 1 foo 2001-01-02 1 False 1
In [237]: dft.dtypes
Out[237]:
A float64
B int64
C object
D datetime64[ns]
E float32
F bool
G int8
dtype: object
On a Series use the dtype method.
In [238]: dft['A'].dtype
Out[238]: dtype('float64')
If a pandas object contains data multiple dtypes IN A SINGLE COLUMN, the dtype of the column will be chosen to accommodate all of the data types (object is the most general).
# these ints are coerced to floats
In [239]: Series([1, 2, 3, 4, 5, 6.])
Out[239]:
0 1
1 2
2 3
3 4
4 5
5 6
dtype: float64
# string data forces an ``object`` dtype
In [240]: Series([1, 2, 3, 6., 'foo'])
Out[240]:
0 1
1 2
2 3
3 6
4 foo
dtype: object
The method get_dtype_counts will return the number of columns of each type in a DataFrame:
In [241]: dft.get_dtype_counts()
Out[241]:
bool 1
datetime64[ns] 1
float32 1
float64 1
int64 1
int8 1
object 1
dtype: int64
Numeric dtypes will propagate and can coexist in DataFrames (starting in v0.11.0). If a dtype is passed (either directly via the dtype keyword, a passed ndarray, or a passed Series, then it will be preserved in DataFrame operations. Furthermore, different numeric dtypes will NOT be combined. The following example will give you a taste.
In [242]: df1 = DataFrame(randn(8, 1), columns = ['A'], dtype = 'float32')
In [243]: df1
Out[243]:
A
0 1.111528
1 -1.805497
2 -0.125340
3 2.055101
4 0.170350
5 -1.551268
6 -0.503071
7 0.370166
In [244]: df1.dtypes
Out[244]:
A float32
dtype: object
In [245]: df2 = DataFrame(dict( A = Series(randn(8),dtype='float16'),
.....: B = Series(randn(8)),
.....: C = Series(np.array(randn(8),dtype='uint8')) ))
.....:
In [246]: df2
Out[246]:
A B C
0 2.220703 0.447712 0
1 0.589355 0.429500 0
2 1.896484 -1.947809 255
3 -0.916992 -0.046360 0
4 0.614746 0.044316 0
5 -0.392578 0.234849 2
6 0.604004 -0.622669 0
7 -0.061737 -0.351207 0
In [247]: df2.dtypes
Out[247]:
A float16
B float64
C uint8
dtype: object
defaults¶
By default integer types are int64 and float types are float64, REGARDLESS of platform (32-bit or 64-bit). The following will all result in int64 dtypes.
In [248]: DataFrame([1, 2], columns=['a']).dtypes
Out[248]:
a int64
dtype: object
In [249]: DataFrame({'a': [1, 2]}).dtypes
Out[249]:
a int64
dtype: object
In [250]: DataFrame({'a': 1 }, index=list(range(2))).dtypes
Out[250]:
a int64
dtype: object
Numpy, however will choose platform-dependent types when creating arrays. The following WILL result in int32 on 32-bit platform.
In [251]: frame = DataFrame(np.array([1, 2]))
upcasting¶
Types can potentially be upcasted when combined with other types, meaning they are promoted from the current type (say int to float)
In [252]: df3 = df1.reindex_like(df2).fillna(value=0.0) + df2
In [253]: df3
Out[253]:
A B C
0 3.332231 0.447712 0
1 -1.216141 0.429500 0
2 1.771144 -1.947809 255
3 1.138109 -0.046360 0
4 0.785096 0.044316 0
5 -1.943846 0.234849 2
6 0.100933 -0.622669 0
7 0.308429 -0.351207 0
In [254]: df3.dtypes
Out[254]:
A float32
B float64
C float64
dtype: object
The values attribute on a DataFrame return the lower-common-denominator of the dtypes, meaning the dtype that can accommodate ALL of the types in the resulting homogenous dtyped numpy array. This can force some upcasting.
In [255]: df3.values.dtype
Out[255]: dtype('float64')
astype¶
You can use the astype method to explicitly convert dtypes from one to another. These will by default return a copy, even if the dtype was unchanged (pass copy=False to change this behavior). In addition, they will raise an exception if the astype operation is invalid.
Upcasting is always according to the numpy rules. If two different dtypes are involved in an operation, then the more general one will be used as the result of the operation.
In [256]: df3
Out[256]:
A B C
0 3.332231 0.447712 0
1 -1.216141 0.429500 0
2 1.771144 -1.947809 255
3 1.138109 -0.046360 0
4 0.785096 0.044316 0
5 -1.943846 0.234849 2
6 0.100933 -0.622669 0
7 0.308429 -0.351207 0
In [257]: df3.dtypes
Out[257]:
A float32
B float64
C float64
dtype: object
# conversion of dtypes
In [258]: df3.astype('float32').dtypes
Out[258]:
A float32
B float32
C float32
dtype: object
object conversion¶
convert_objects is a method to try to force conversion of types from the object dtype to other types. To force conversion of specific types that are number like, e.g. could be a string that represents a number, pass convert_numeric=True. This will force strings and numbers alike to be numbers if possible, otherwise they will be set to np.nan.
In [259]: df3['D'] = '1.'
In [260]: df3['E'] = '1'
In [261]: df3.convert_objects(convert_numeric=True).dtypes
Out[261]:
A float32
B float64
C float64
D float64
E int64
dtype: object
# same, but specific dtype conversion
In [262]: df3['D'] = df3['D'].astype('float16')
In [263]: df3['E'] = df3['E'].astype('int32')
In [264]: df3.dtypes
Out[264]:
A float32
B float64
C float64
D float16
E int32
dtype: object
To force conversion to datetime64[ns], pass convert_dates='coerce'. This will convert any datetime-like object to dates, forcing other values to NaT. This might be useful if you are reading in data which is mostly dates, but occasionally has non-dates intermixed and you want to represent as missing.
In [265]: s = Series([datetime(2001,1,1,0,0),
.....: 'foo', 1.0, 1, Timestamp('20010104'),
.....: '20010105'],dtype='O')
.....:
In [266]: s
Out[266]:
0 2001-01-01 00:00:00
1 foo
2 1
3 1
4 2001-01-04 00:00:00
5 20010105
dtype: object
In [267]: s.convert_objects(convert_dates='coerce')
Out[267]:
0 2001-01-01
1 NaT
2 NaT
3 NaT
4 2001-01-04
5 2001-01-05
dtype: datetime64[ns]
In addition, convert_objects will attempt the soft conversion of any object dtypes, meaning that if all the objects in a Series are of the same type, the Series will have that dtype.
gotchas¶
Performing selection operations on integer type data can easily upcast the data to floating. The dtype of the input data will be preserved in cases where nans are not introduced (starting in 0.11.0) See also integer na gotchas
In [268]: dfi = df3.astype('int32')
In [269]: dfi['E'] = 1
In [270]: dfi
Out[270]:
A B C D E
0 3 0 0 1 1
1 -1 0 0 1 1
2 1 -1 255 1 1
3 1 0 0 1 1
4 0 0 0 1 1
5 -1 0 2 1 1
6 0 0 0 1 1
7 0 0 0 1 1
In [271]: dfi.dtypes
Out[271]:
A int32
B int32
C int32
D int32
E int64
dtype: object
In [272]: casted = dfi[dfi>0]
In [273]: casted
Out[273]:
A B C D E
0 3 NaN NaN 1 1
1 NaN NaN NaN 1 1
2 1 NaN 255 1 1
3 1 NaN NaN 1 1
4 NaN NaN NaN 1 1
5 NaN NaN 2 1 1
6 NaN NaN NaN 1 1
7 NaN NaN NaN 1 1
In [274]: casted.dtypes
Out[274]:
A float64
B float64
C float64
D int32
E int64
dtype: object
While float dtypes are unchanged.
In [275]: dfa = df3.copy()
In [276]: dfa['A'] = dfa['A'].astype('float32')
In [277]: dfa.dtypes
Out[277]:
A float32
B float64
C float64
D float16
E int32
dtype: object
In [278]: casted = dfa[df2>0]
In [279]: casted
Out[279]:
A B C D E
0 3.332231 0.447712 NaN NaN NaN
1 -1.216141 0.429500 NaN NaN NaN
2 1.771144 NaN 255 NaN NaN
3 NaN NaN NaN NaN NaN
4 0.785096 0.044316 NaN NaN NaN
5 NaN 0.234849 2 NaN NaN
6 0.100933 NaN NaN NaN NaN
7 NaN NaN NaN NaN NaN
In [280]: casted.dtypes
Out[280]:
A float32
B float64
C float64
D float16
E float64
dtype: object
Selecting columns based on dtype¶
New in version 0.14.1.
The select_dtypes() method implements subsetting of columns based on their dtype.
First, let’s create a DataFrame with a slew of different dtypes:
In [281]: df = DataFrame({'string': list('abc'),
.....: 'int64': list(range(1, 4)),
.....: 'uint8': np.arange(3, 6).astype('u1'),
.....: 'float64': np.arange(4.0, 7.0),
.....: 'bool1': [True, False, True],
.....: 'bool2': [False, True, False],
.....: 'dates': pd.date_range('now', periods=3).values})
.....:
In [282]: df['tdeltas'] = df.dates.diff()
In [283]: df['uint64'] = np.arange(3, 6).astype('u8')
In [284]: df['other_dates'] = pd.date_range('20130101', periods=3).values
In [285]: df
Out[285]:
bool1 bool2 dates float64 int64 string uint8 tdeltas \
0 True False 2014-07-11 09:13:45 4 1 a 3 NaT
1 False True 2014-07-12 09:13:45 5 2 b 4 1 days
2 True False 2014-07-13 09:13:45 6 3 c 5 1 days
uint64 other_dates
0 3 2013-01-01
1 4 2013-01-02
2 5 2013-01-03
select_dtypes has two parameters include and exclude that allow you to say “give me the columns WITH these dtypes” (include) and/or “give the columns WITHOUT these dtypes” (exclude).
For example, to select bool columns
In [286]: df.select_dtypes(include=[bool])
Out[286]:
bool1 bool2
0 True False
1 False True
2 True False
You can also pass the name of a dtype in the numpy dtype hierarchy:
In [287]: df.select_dtypes(include=['bool'])
Out[287]:
bool1 bool2
0 True False
1 False True
2 True False
select_dtypes() also works with generic dtypes as well.
For example, to select all numeric and boolean columns while excluding unsigned integers
In [288]: df.select_dtypes(include=['number', 'bool'], exclude=['unsignedinteger'])
Out[288]:
bool1 bool2 float64 int64 tdeltas
0 True False 4 1 NaT
1 False True 5 2 1 days
2 True False 6 3 1 days
To select string columns you must use the object dtype:
In [289]: df.select_dtypes(include=['object'])
Out[289]:
string
0 a
1 b
2 c
To see all the child dtypes of a generic dtype like numpy.number you can define a function that returns a tree of child dtypes:
In [290]: def subdtypes(dtype):
.....: subs = dtype.__subclasses__()
.....: if not subs:
.....: return dtype
.....: return [dtype, [subdtypes(dt) for dt in subs]]
.....:
All numpy dtypes are subclasses of numpy.generic:
In [291]: subdtypes(np.generic)
Out[291]:
[numpy.generic,
[[numpy.number,
[[numpy.integer,
[[numpy.signedinteger,
[numpy.int8,
numpy.int16,
numpy.int32,
numpy.int32,
numpy.int64,
numpy.timedelta64]],
[numpy.unsignedinteger,
[numpy.uint8,
numpy.uint16,
numpy.uint32,
numpy.uint32,
numpy.uint64]]]],
[numpy.inexact,
[[numpy.floating,
[numpy.float16, numpy.float32, numpy.float64, numpy.float96]],
[numpy.complexfloating,
[numpy.complex64, numpy.complex128, numpy.complex192]]]]]],
[numpy.flexible,
[[numpy.character, [numpy.string_, numpy.unicode_]],
[numpy.void, [numpy.core.records.record]]]],
numpy.bool_,
numpy.datetime64,
numpy.object_]]
Note
The include and exclude parameters must be non-string sequences.