Pandas is an amazing data analysis toolkit for Python. It is designed to operate on relational or labeled data and gives you tools to slice and dice as you please.
In this two-part tutorial, you'll learn about the fundamental data structures of Pandas: the series and the data frame. You'll also learn how to select data, deal with missing values, manipulate your data, merge your data, group your data, work with time series, and even plot data.
Installation
To install, just pip install pandas. It will take care of installing numpy too if you don't have it installed.
Series
Pandas series are typed and labeled 1-D arrays. This means that each element can be accessed by its label in addition to its index.
Here is a series of integers where the labels are Roman numerals. You can index and slice using the labels or integer indices. Unlike with regular Python list slicing, when using labels the last item is included!
>>> s = pd.Series(np.arange(1,5), ['I', 'II', 'III', 'IV', 'V']) >>> s['III'] 3 >>> s[0] 1 >>> s['II':'V'] II 2 III 3 IV 4 V 5 >>> s[1:5] II 2 III 3 IV 4 V 5
If you don't provide an index then a 0-based integer index is automatically created:
>>> s = pd.Series((50, 7, 88, 9)) >>> s 0 50 1 7 2 88 3 9
Now, here is a little secret for you. Pandas series are a wrapper around Numpy's arrays.
>>> s.values array([50, 7, 88, 9]) >>> type(s.values) <class 'numpy.ndarray'>
Unlike Python lists or numpy arrays, operations on series align on the index. If the indexes don't match then the union of indices will be used with missing values as appropriate. Here are a few examples using dicts as data so the keys become the series index:
>>> s1 = pd.Series(dict(a=1, b=2, c=3)) >>> s2 = pd.Series(dict(a=4, b=5, c=6, d=7)) >>> s1 + s2 a 5.0 b 7.0 c 9.0 d NaN >>> s1[1:] * s2[:-1] a NaN b 10.0 c 18.0
Data Frames
Data frames are the primary pandas data structure. They represent tables of data where each column is a series. Data frames have an index too, which serves as a row label. A data frame also has column labels. Here is how to declare a data frame using a dict.
>>> df = pd.DataFrame(dict(a=[1, 2, 3],
b=[4,5,6],
c=pd.Timestamp('20170902'),
d=pd.Categorical(['red',
'green',
'blue'])))
>>> df
a b c d
0 1 4 2017-09-02 red
1 2 5 2017-09-02 green
2 3 6 2017-09-02 blue
Note that an integer index (row label) was created automatically. You can of course provide your own index:
>>> df.index = ('I II III'.split())
>>> df
a b c d
I 1 4 2017-09-02 red
II 2 5 2017-09-02 green
III 3 6 2017-09-02 blue
Importing and Exporting Data
Data frames can be constructed from a very wide variety of sources:
- dict of 1-D ndarrays, lists, dicts, or series
- 2-D numpy.ndarray
- structured or record ndarray
- another DataFrame
You can also import or load data from many file formats and databases such as:
- CSV
- Excel
- HTML
- HDFStore
- SQL
Here is how to read a CSV file:
data.csv
--------
I,1,4,2017-09-02,red
II,2,5,2017-09-02,green
III,3,6,2017-09-02,blue
>>> pd.read_csv('data.csv')
I 1 4 2017-09-02 red
0 II 2 5 2017-09-02 green
1 III 3 6 2017-09-02 blue
Here is the complete list of read_functions():
>>> read_functions = [a for a in dir(pd) if a.startswith('read_')]
>>> print('\n'.join(read_functions))
read_clipboard
read_csv
read_excel
read_feather
read_fwf
read_gbq
read_hdf
read_html
read_json
read_msgpack
read_pickle
read_sas
read_sql
read_sql_query
read_sql_table
read_stata
read_table
There are corresponding methods on the data frame object itself for exporting the data to many formats and databases. Here is how you export to json and msgpack:
>>> df.to_json()
'{"a":{"I":1,"II":2,"III":3},
"b":{"I":4,"II":5,"III":6},
"c":{"I":1504310400000,"II":1504310400000,"III":1504310400000},
"d":{"I":"red","II":"green","III":"blue"}}'
>>> df.to_msgpack()
b'\x84\xa3typ\xadblock_manager\xa5klass\xa9DataFrame\xa4axes
\x92\x86\xa3typ\xa5index\xa5klass\xa5Index\xa4name\xc0\xa5dtype
\xa6object\xa4data\x94\xa1a\xa1b\xa1c\xa1d\xa8compress\xc0\x86
\xa3typ\xa5index\xa5klass\xa5Index\xa4name\xc0\xa5dtype
\xa6object\xa4data\x93\xa1I\xa2II\xa3III\xa8compress\xc0
\xa6blocks\x93\x86\xa4locs\x86\xa3typ\xa7ndarray\xa5shape\x91
\x02\xa4ndim\x01\xa5dtype\xa5int64\xa4data\xd8\x00\x00\x00\x00
\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00\xa8compress
\xc0\xa6values\xc70\x00\x01\x00\x00\x00\x00\x00\x00\x00\x02\x00
\x00\x00\x00\x00\x00\x00\x03\x00\x00\x00\x00\x00\x00\x00\x04
\x00\x00\x00\x00\x00\x00\x00\x05\x00\x00\x00\x00\x00\x00\x00
\x06\x00\x00\x00\x00\x00\x00\x00\xa5shape\x92\x02\x03\xa5dtype
\xa5int64\xa5klass\xa8IntBlock\xa8compress\xc0\x86\xa4locs\x86
\xa3typ\xa7ndarray\xa5shape\x91\x01\xa4ndim\x01\xa5dtype
\xa5int64\xa4data\xd7\x00\x02\x00\x00\x00\x00\x00\x00\x00
\xa8compress\xc0\xa6values\xc7\x18\x00\x00\x00\xed\xafVb\xe0
\x14\x00\x00\xed\xafVb\xe0\x14\x00\x00\xed\xafVb\xe0\x14
\xa5shape\x92\x01\x03\xa5dtype\xaedatetime64[ns]\xa5klass
\xadDatetimeBlock\xa8compress\xc0\x86\xa4locs\x86\xa3typ
\xa7ndarray\xa5shape\x91\x01\xa4ndim\x01\xa5dtype\xa5int64
\xa4data\xd7\x00\x03\x00\x00\x00\x00\x00\x00\x00\xa8compress
\xc0\xa6values\x87\xa3typ\xa8category\xa5klass\xabCategorical
\xa4name\xc0\xa5codes\x86\xa3typ\xa7ndarray\xa5shape\x91\x03
\xa4ndim\x01\xa5dtype\xa4int8\xa4data\xc7\x03\x00\x02\x01\x00
\xa8compress\xc0\xaacategories\x86\xa3typ\xa5index\xa5klass
\xa5Index\xa4name\xc0\xa5dtype\xa6object\xa4data\x93\xa4blue
\xa5green\xa3red\xa8compress\xc0\xa7ordered\xc2\xa8compress
\xc0\xa5shape\x91\x03\xa5dtype\xa8category\xa5klass
\xb0CategoricalBlock\xa8compress\xc0'
Metadata and Stats
Pandas gives a lot of information about data frames. Check out these methods:
>>> df.index
Index(['I', 'II', 'III'], dtype='object')
>>> df.columns
Index(['a', 'b', 'c', 'd'], dtype='object')
>>> df.describe()
a b
count 3.0 3.0
mean 2.0 5.0
std 1.0 1.0
min 1.0 4.0
25% 1.5 4.5
50% 2.0 5.0
75% 2.5 5.5
max 3.0 6.
Selecting Data
Data frames let you select data. If you want to select a row by index, you need to use the loc attribute. To select columns, you simply use the column name. Here is how to select individual rows, individual columns, a slice of rows, a slice of columns, and last but not least, a rectangular section (subset of rows and subset of columns from these rows):
Single row
----------
>>> df.loc['II']
a 2
b 5
c 2017-09-02 00:00:00
d green
Multiple rows using integer index (no 'loc')
--------------------------------------------
>>> df[:2]
a b c d
I 1 4 2017-09-02 red
II 2 5 2017-09-02 green
Single column
-------------
>>> df['b']
I 4
II 5
III 6
Multiple columns
----------------
>>> df.loc[:, 'b':'c']
b c
I 4 2017-09-02
II 5 2017-09-02
III 6 2017-09-02
Rectangular section
-------------------
>>> df.loc[:'II', 'b':'c']
b c
I 4 2017-09-02
II 5 2017-09-02
Using integer index (when actual index is not integer)
------------------------------------------------------
>>> df.iloc[:2, 1:3]
b c
I 4 2017-09-02
II 5 2017-09-02
In addition to those direct addressing data selections, you can also select based on values. For example, you can select only rows with even values in column b:
>>> df[df.b % 2 == 0]
a b c d
I 1 4 2017-09-02 red
III 3 6 2017-09-02 blue
Sorting Data
Pandas gives you sorting too. Let's sort the following data frame by index (rows) and by column. Multiple-level indexing is supported too:
index=['one', 'two', 'three', 'four', 'five']
df = pd.DataFrame(np.random.randn(5,2),
index=index,
columns=['a','b'])
Sort by index (alphabetically and descending)
---------------------------------------------
>>> df.sort_index(ascending=False)
a b
two -0.689523 1.411403
three 0.332707 0.307561
one -0.042172 0.374922
four 0.426519 -0.425181
five -0.161095 -0.849932
Sort by column
--------------
>>> df.sort_values(by='a')
a b
two -0.689523 1.411403
five -0.161095 -0.849932
one -0.042172 0.374922
three 0.332707 0.307561
four 0.426519 -0.425181
Conclusion
In this part of the tutorial, we covered the basic data types of Pandas: the series and the data frame. We imported and exported data, selected subsets of data, worked with metadata, and sorted the data. In part two, we'll continue our journey and deal with missing data, data manipulation, data merging, data grouping, time series, and plotting. Stay tuned.
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