pandas
python
import numpy as np np.set_printoptions(precision=4, suppress=True) import pandas as pd pd.set_option('display.precision', 4, 'display.max_columns', 8) pd.options.display.max_rows = 15
import matplotlib matplotlib.style.use('ggplot') import matplotlib.pyplot as plt plt.close('all')
We'll start with a quick, non-comprehensive overview of the fundamental data structures in pandas to get you started. The fundamental behavior about data types, indexing, and axis labeling / alignment apply across all of the objects. To get started, import numpy and load pandas into your namespace:
python
import numpy as np import pandas as pd
Here is a basic tenet to keep in mind: data alignment is intrinsic. The link between labels and data will not be broken unless done so explicitly by you.
We'll give a brief intro to the data structures, then consider all of the broad categories of functionality and methods in separate sections.
Series is a one-dimensional labeled array capable of holding any data type (integers, strings, floating point numbers, Python objects, etc.). The axis labels are collectively referred to as the index. The basic method to create a Series is to call:
>>> s = pd.Series(data, index=index)Here, data can be many different things:
- a Python dict
- an ndarray
- a scalar value (like 5)
The passed index is a list of axis labels. Thus, this separates into a few cases depending on what data is:
From ndarray
If data is an ndarray, index must be the same length as data. If no index is passed, one will be created having values [0, ..., len(data) - 1].
python
s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e']) s s.index
pd.Series(np.random.randn(5))
Note
Starting in v0.8.0, pandas supports non-unique index values. If an operation that does not support duplicate index values is attempted, an exception will be raised at that time. The reason for being lazy is nearly all performance-based (there are many instances in computations, like parts of GroupBy, where the index is not used).
From dict
If data is a dict, if index is passed the values in data corresponding to the labels in the index will be pulled out. Otherwise, an index will be constructed from the sorted keys of the dict, if possible.
python
d = {'a' : 0., 'b' : 1., 'c' : 2.} pd.Series(d) pd.Series(d, index=['b', 'c', 'd', 'a'])
Note
NaN (not a number) is the standard missing data marker used in pandas
From scalar value If data is a scalar value, an index must be provided. The value will be repeated to match the length of index
python
pd.Series(5., index=['a', 'b', 'c', 'd', 'e'])
Series acts very similarly to a ndarray, and is a valid argument to most NumPy functions. However, things like slicing also slice the index.
We will address array-based indexing in a separate section <indexing>.
A Series is like a fixed-size dict in that you can get and set values by index label:
If a label is not contained, an exception is raised:
>>> s['f']
KeyError: 'f'Using the get method, a missing label will return None or specified default:
python
s.get('f')
s.get('f', np.nan)
See also the section on attribute access<indexing.attribute_access>.
When doing data analysis, as with raw NumPy arrays looping through Series value-by-value is usually not necessary. Series can be also be passed into most NumPy methods expecting an ndarray.
python
s + s s * 2 np.exp(s)
A key difference between Series and ndarray is that operations between Series automatically align the data based on label. Thus, you can write computations without giving consideration to whether the Series involved have the same labels.
python
s[1:] + s[:-1]
The result of an operation between unaligned Series will have the union of the indexes involved. If a label is not found in one Series or the other, the result will be marked as missing NaN. Being able to write code without doing any explicit data alignment grants immense freedom and flexibility in interactive data analysis and research. The integrated data alignment features of the pandas data structures set pandas apart from the majority of related tools for working with labeled data.
Note
In general, we chose to make the default result of operations between differently indexed objects yield the union of the indexes in order to avoid loss of information. Having an index label, though the data is missing, is typically important information as part of a computation. You of course have the option of dropping labels with missing data via the dropna function.
Series can also have a name attribute:
python
s = pd.Series(np.random.randn(5), name='something') s s.name
The Series name will be assigned automatically in many cases, in particular when taking 1D slices of DataFrame as you will see below.
0.18.0
You can rename a Series with the pandas.Series.rename method.
python
s2 = s.rename("different") s2.name
Note that s and s2 refer to different objects.
DataFrame is a 2-dimensional labeled data structure with columns of potentially different types. You can think of it like a spreadsheet or SQL table, or a dict of Series objects. It is generally the most commonly used pandas object. Like Series, DataFrame accepts many different kinds of input:
- Dict of 1D ndarrays, lists, dicts, or Series
- 2-D numpy.ndarray
- Structured or record ndarray
- A
Series- Another
DataFrame
Along with the data, you can optionally pass index (row labels) and columns (column labels) arguments. If you pass an index and / or columns, you are guaranteeing the index and / or columns of the resulting DataFrame. Thus, a dict of Series plus a specific index will discard all data not matching up to the passed index.
If axis labels are not passed, they will be constructed from the input data based on common sense rules.
The result index will be the union of the indexes of the various Series. If there are any nested dicts, these will be first converted to Series. If no columns are passed, the columns will be the sorted list of dict keys.
python
- d = {'one' : pd.Series([1., 2., 3.], index=['a', 'b', 'c']),
'two' : pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])}
df = pd.DataFrame(d) df
pd.DataFrame(d, index=['d', 'b', 'a']) pd.DataFrame(d, index=['d', 'b', 'a'], columns=['two', 'three'])
The row and column labels can be accessed respectively by accessing the index and columns attributes:
Note
When a particular set of columns is passed along with a dict of data, the passed columns override the keys in the dict.
python
df.index df.columns
The ndarrays must all be the same length. If an index is passed, it must clearly also be the same length as the arrays. If no index is passed, the result will be range(n), where n is the array length.
python
- d = {'one' : [1., 2., 3., 4.],
'two' : [4., 3., 2., 1.]}
pd.DataFrame(d) pd.DataFrame(d, index=['a', 'b', 'c', 'd'])
This case is handled identically to a dict of arrays.
python
data = np.zeros((2,), dtype=[('A', 'i4'),('B', 'f4'),('C', 'a10')]) data[:] = [(1,2.,'Hello'), (2,3.,"World")]
pd.DataFrame(data) pd.DataFrame(data, index=['first', 'second']) pd.DataFrame(data, columns=['C', 'A', 'B'])
Note
DataFrame is not intended to work exactly like a 2-dimensional NumPy ndarray.
python
data2 = [{'a': 1, 'b': 2}, {'a': 5, 'b': 10, 'c': 20}] pd.DataFrame(data2) pd.DataFrame(data2, index=['first', 'second']) pd.DataFrame(data2, columns=['a', 'b'])
You can automatically create a multi-indexed frame by passing a tuples dictionary
python
- pd.DataFrame({('a', 'b'): {('A', 'B'): 1, ('A', 'C'): 2},
('a', 'a'): {('A', 'C'): 3, ('A', 'B'): 4}, ('a', 'c'): {('A', 'B'): 5, ('A', 'C'): 6}, ('b', 'a'): {('A', 'C'): 7, ('A', 'B'): 8}, ('b', 'b'): {('A', 'D'): 9, ('A', 'B'): 10}})
The result will be a DataFrame with the same index as the input Series, and with one column whose name is the original name of the Series (only if no other column name provided).
Missing Data
Much more will be said on this topic in the Missing data <missing_data> section. To construct a DataFrame with missing data, use np.nan for those values which are missing. Alternatively, you may pass a numpy.MaskedArray as the data argument to the DataFrame constructor, and its masked entries will be considered missing.
DataFrame.from_dict
DataFrame.from_dict takes a dict of dicts or a dict of array-like sequences and returns a DataFrame. It operates like the DataFrame constructor except for the orient parameter which is 'columns' by default, but which can be set to 'index' in order to use the dict keys as row labels.
DataFrame.from_records
DataFrame.from_records takes a list of tuples or an ndarray with structured dtype. Works analogously to the normal DataFrame constructor, except that index maybe be a specific field of the structured dtype to use as the index. For example:
python
data pd.DataFrame.from_records(data, index='C')
DataFrame.from_items
DataFrame.from_items works analogously to the form of the dict constructor that takes a sequence of (key, value) pairs, where the keys are column (or row, in the case of orient='index') names, and the value are the column values (or row values). This can be useful for constructing a DataFrame with the columns in a particular order without having to pass an explicit list of columns:
python
pd.DataFrame.from_items([('A', [1, 2, 3]), ('B', [4, 5, 6])])
If you pass orient='index', the keys will be the row labels. But in this case you must also pass the desired column names:
python
- pd.DataFrame.from_items([('A', [1, 2, 3]), ('B', [4, 5, 6])],
orient='index', columns=['one', 'two', 'three'])
You can treat a DataFrame semantically like a dict of like-indexed Series objects. Getting, setting, and deleting columns works with the same syntax as the analogous dict operations:
python
df['one'] df['three'] = df['one'] * df['two'] df['flag'] = df['one'] > 2 df
Columns can be deleted or popped like with a dict:
python
del df['two'] three = df.pop('three') df
When inserting a scalar value, it will naturally be propagated to fill the column:
python
df['foo'] = 'bar' df
When inserting a Series that does not have the same index as the DataFrame, it will be conformed to the DataFrame's index:
python
df['one_trunc'] = df['one'][:2] df
You can insert raw ndarrays but their length must match the length of the DataFrame's index.
By default, columns get inserted at the end. The insert function is available to insert at a particular location in the columns:
python
df.insert(1, 'bar', df['one']) df
0.16.0
Inspired by dplyr's mutate verb, DataFrame has an ~pandas.DataFrame.assign method that allows you to easily create new columns that are potentially derived from existing columns.
python
iris = pd.read_csv('data/iris.data') iris.head()
- (iris.assign(sepal_ratio = iris['SepalWidth'] / iris['SepalLength'])
.head())
Above was an example of inserting a precomputed value. We can also pass in a function of one argument to be evalutated on the DataFrame being assigned to.
python
- iris.assign(sepal_ratio = lambda x: (x['SepalWidth'] /
x['SepalLength'])).head()
assign always returns a copy of the data, leaving the original DataFrame untouched.
Passing a callable, as opposed to an actual value to be inserted, is useful when you don't have a reference to the DataFrame at hand. This is common when using assign in chains of operations. For example, we can limit the DataFrame to just those observations with a Sepal Length greater than 5, calculate the ratio, and plot:
python
@savefig basics_assign.png (iris.query('SepalLength > 5') .assign(SepalRatio = lambda x: x.SepalWidth / x.SepalLength, PetalRatio = lambda x: x.PetalWidth / x.PetalLength) .plot(kind='scatter', x='SepalRatio', y='PetalRatio'))
Since a function is passed in, the function is computed on the DataFrame being assigned to. Importantly, this is the DataFrame that's been filtered to those rows with sepal length greater than 5. The filtering happens first, and then the ratio calculations. This is an example where we didn't have a reference to the filtered DataFrame available.
The function signature for assign is simply **kwargs. The keys are the column names for the new fields, and the values are either a value to be inserted (for example, a Series or NumPy array), or a function of one argument to be called on the DataFrame. A copy of the original DataFrame is returned, with the new values inserted.
Warning
Since the function signature of assign is **kwargs, a dictionary, the order of the new columns in the resulting DataFrame cannot be guaranteed to match the order you pass in. To make things predictable, items are inserted alphabetically (by key) at the end of the DataFrame.
All expressions are computed first, and then assigned. So you can't refer to another column being assigned in the same call to assign. For example:
- In [1]: # Don't do this, bad reference to C
- df.assign(C = lambda x: x['A'] + x['B'],
D = lambda x: x['A'] + x['C'])
- In [2]: # Instead, break it into two assigns
- (df.assign(C = lambda x: x['A'] + x['B'])
.assign(D = lambda x: x['A'] + x['C']))
The basics of indexing are as follows:
| Operation | Syntax | Result |
|---|---|---|
| Select column | df[col] |
Series |
| Select row by label | df.loc[label] |
Series |
| Select row by integer location | df.iloc[loc] |
Series |
| Slice rows | df[5:10] |
DataFrame |
| Select rows by boolean vector | df[bool_vec] |
DataFrame |
Row selection, for example, returns a Series whose index is the columns of the DataFrame:
python
df.loc['b'] df.iloc[2]
For a more exhaustive treatment of more sophisticated label-based indexing and slicing, see the section on indexing <indexing>. We will address the fundamentals of reindexing / conforming to new sets of labels in the section on reindexing <basics.reindexing>.
Data alignment between DataFrame objects automatically align on both the columns and the index (row labels). Again, the resulting object will have the union of the column and row labels.
python
df = pd.DataFrame(np.random.randn(10, 4), columns=['A', 'B', 'C', 'D']) df2 = pd.DataFrame(np.random.randn(7, 3), columns=['A', 'B', 'C']) df + df2
When doing an operation between DataFrame and Series, the default behavior is to align the Series index on the DataFrame columns, thus broadcasting row-wise. For example:
python
df - df.iloc[0]
In the special case of working with time series data, and the DataFrame index also contains dates, the broadcasting will be column-wise:
python
index = pd.date_range('1/1/2000', periods=8) df = pd.DataFrame(np.random.randn(8, 3), index=index, columns=list('ABC')) df type(df['A']) df - df['A']
Warning
df - df['A']is now deprecated and will be removed in a future release. The preferred way to replicate this behavior is
df.sub(df['A'], axis=0)For explicit control over the matching and broadcasting behavior, see the section on flexible binary operations <basics.binop>.
Operations with scalars are just as you would expect:
python
df * 5 + 2 1 / df df ** 4
Boolean operators work as well:
python
df1 = pd.DataFrame({'a' : [1, 0, 1], 'b' : [0, 1, 1] }, dtype=bool) df2 = pd.DataFrame({'a' : [0, 1, 1], 'b' : [1, 1, 0] }, dtype=bool) df1 & df2 df1 | df2 df1 ^ df2 -df1
To transpose, access the T attribute (also the transpose function), similar to an ndarray:
python
# only show the first 5 rows df[:5].T
Elementwise NumPy ufuncs (log, exp, sqrt, ...) and various other NumPy functions can be used with no issues on DataFrame, assuming the data within are numeric:
python
np.exp(df) np.asarray(df)
The dot method on DataFrame implements matrix multiplication:
python
df.T.dot(df)
Similarly, the dot method on Series implements dot product:
python
s1 = pd.Series(np.arange(5,10)) s1.dot(s1)
DataFrame is not intended to be a drop-in replacement for ndarray as its indexing semantics are quite different in places from a matrix.
Very large DataFrames will be truncated to display them in the console. You can also get a summary using ~pandas.DataFrame.info. (Here I am reading a CSV version of the baseball dataset from the plyr R package):
python
# force a summary to be printed pd.set_option('display.max_rows', 5)
python
baseball = pd.read_csv('data/baseball.csv') print(baseball) baseball.info()
python
# restore GlobalPrintConfig pd.reset_option('^display.')
However, using to_string will return a string representation of the DataFrame in tabular form, though it won't always fit the console width:
python
print(baseball.iloc[-20:, :12].to_string())
New since 0.10.0, wide DataFrames will now be printed across multiple rows by default:
python
pd.DataFrame(np.random.randn(3, 12))
You can change how much to print on a single row by setting the display.width option:
python
pd.set_option('display.width', 40) # default is 80
pd.DataFrame(np.random.randn(3, 12))
You can adjust the max width of the individual columns by setting display.max_colwidth
python
- datafile={'filename': ['filename_01','filename_02'],
- 'path': ["media/user_name/storage/folder_01/filename_01",
"media/user_name/storage/folder_02/filename_02"]}
pd.set_option('display.max_colwidth',30) pd.DataFrame(datafile)
pd.set_option('display.max_colwidth',100) pd.DataFrame(datafile)
python
pd.reset_option('display.width') pd.reset_option('display.max_colwidth')
You can also disable this feature via the expand_frame_repr option. This will print the table in one block.
If a DataFrame column label is a valid Python variable name, the column can be accessed like attributes:
python
- df = pd.DataFrame({'foo1' : np.random.randn(5),
'foo2' : np.random.randn(5)})
df df.foo1
The columns are also connected to the IPython completion mechanism so they can be tab-completed:
In [5]: df.fo<TAB>
df.foo1 df.foo2Panel is a somewhat less-used, but still important container for 3-dimensional data. The term panel data is derived from econometrics and is partially responsible for the name pandas: pan(el)-da(ta)-s. The names for the 3 axes are intended to give some semantic meaning to describing operations involving panel data and, in particular, econometric analysis of panel data. However, for the strict purposes of slicing and dicing a collection of DataFrame objects, you may find the axis names slightly arbitrary:
- items: axis 0, each item corresponds to a DataFrame contained inside
- major_axis: axis 1, it is the index (rows) of each of the DataFrames
- minor_axis: axis 2, it is the columns of each of the DataFrames
Construction of Panels works about like you would expect:
python
- wp = pd.Panel(np.random.randn(2, 5, 4), items=['Item1', 'Item2'],
major_axis=pd.date_range('1/1/2000', periods=5), minor_axis=['A', 'B', 'C', 'D'])
wp
python
- data = {'Item1' : pd.DataFrame(np.random.randn(4, 3)),
'Item2' : pd.DataFrame(np.random.randn(4, 2))}
pd.Panel(data)
Note that the values in the dict need only be convertible to DataFrame. Thus, they can be any of the other valid inputs to DataFrame as per above.
One helpful factory method is Panel.from_dict, which takes a dictionary of DataFrames as above, and the following named parameters:
| Parameter | Default | Description |
|---|---|---|
| intersect | False |
drops elements whose indices do not align |
| orient | items |
use minor to use DataFrames' columns as panel items |
For example, compare to the construction above:
python
pd.Panel.from_dict(data, orient='minor')
Orient is especially useful for mixed-type DataFrames. If you pass a dict of DataFrame objects with mixed-type columns, all of the data will get upcasted to dtype=object unless you pass orient='minor':
python
- df = pd.DataFrame({'a': ['foo', 'bar', 'baz'],
'b': np.random.randn(3)})
df data = {'item1': df, 'item2': df} panel = pd.Panel.from_dict(data, orient='minor') panel['a'] panel['b'] panel['b'].dtypes
Note
Unfortunately Panel, being less commonly used than Series and DataFrame, has been slightly neglected feature-wise. A number of methods and options available in DataFrame are not available in Panel. This will get worked on, of course, in future releases. And faster if you join me in working on the codebase.
This method was introduced in v0.7 to replace LongPanel.to_long, and converts a DataFrame with a two-level index to a Panel.
python
midx = pd.MultiIndex(levels=[['one', 'two'], ['x','y']], labels=[[1,1,0,0],[1,0,1,0]]) df = pd.DataFrame({'A' : [1, 2, 3, 4], 'B': [5, 6, 7, 8]}, index=midx) df.to_panel()
Similar to DataFrame functioning as a dict of Series, Panel is like a dict of DataFrames:
python
wp['Item1'] wp['Item3'] = wp['Item1'] / wp['Item2']
The API for insertion and deletion is the same as for DataFrame. And as with DataFrame, if the item is a valid python identifier, you can access it as an attribute and tab-complete it in IPython.
A Panel can be rearranged using its transpose method (which does not make a copy by default unless the data are heterogeneous):
python
wp.transpose(2, 0, 1)
| Operation | Syntax | Result |
|---|---|---|
| Select item | wp[item] |
DataFrame |
| Get slice at major_axis label | wp.major_xs(val) |
DataFrame |
| Get slice at minor_axis label | wp.minor_xs(val) |
DataFrame |
For example, using the earlier example data, we could do:
python
wp['Item1'] wp.major_xs(wp.major_axis[2]) wp.minor_axis wp.minor_xs('C')
Another way to change the dimensionality of an object is to squeeze a 1-len object, similar to wp['Item1']
python
wp.reindex(items=['Item1']).squeeze() wp.reindex(items=['Item1'], minor=['B']).squeeze()
A Panel can be represented in 2D form as a hierarchically indexed DataFrame. See the section hierarchical indexing <advanced.hierarchical> for more on this. To convert a Panel to a DataFrame, use the to_frame method:
python
- panel = pd.Panel(np.random.randn(3, 5, 4), items=['one', 'two', 'three'],
major_axis=pd.date_range('1/1/2000', periods=5), minor_axis=['a', 'b', 'c', 'd'])
panel.to_frame()
Warning
In 0.19.0 Panel4D and PanelND are deprecated and will be removed in a future version. The recommended way to represent these types of n-dimensional data are with the xarray package. Pandas provides a ~Panel4D.to_xarray method to automate this conversion.
See the docs of a previous version for documentation on these objects.