This is a repository for short and sweet examples and links for useful pandas recipes. We encourage users to add to this documentation.

This is a great First Pull Request (to add interesting links and/or put short code inline for existing links)

Missing Data

The missing data docs.

Fill forward a reversed timeseries

In [1]: df = pd.DataFrame(np.random.randn(6,1), index=pd.date_range('2013-08-01', periods=6, freq='B'), columns=list('A'))

In [2]: df.ix[3,'A'] = np.nan

In [3]: df
2013-08-01  0.469112
2013-08-02 -0.282863
2013-08-05 -1.509059
2013-08-06       NaN
2013-08-07  1.212112
2013-08-08 -0.173215

In [4]: df.reindex(df.index[::-1]).ffill()
2013-08-08 -0.173215
2013-08-07  1.212112
2013-08-06  1.212112
2013-08-05 -1.509059
2013-08-02 -0.282863
2013-08-01  0.469112

cumsum reset at NaN values


The grouping docs.

Basic grouping with apply

Using get_group

Apply to different items in a group

Expanding Apply

Replacing values with groupby means

Sort by group with aggregation

Create multiple aggregated columns

Create a value counts column and reassign back to the DataFrame

Shift groups of the values in a column based on the index

In [5]: df = pd.DataFrame(
   ...:      {u'line_race': [10L, 10L, 8L, 10L, 10L, 8L],
   ...:       u'beyer': [99L, 102L, 103L, 103L, 88L, 100L]},
   ...:      index=[u'Last Gunfighter', u'Last Gunfighter', u'Last Gunfighter',
   ...:             u'Paynter', u'Paynter', u'Paynter']); df
                 beyer  line_race
Last Gunfighter     99         10
Last Gunfighter    102         10
Last Gunfighter    103          8
Paynter            103         10
Paynter             88         10
Paynter            100          8

In [6]: df['beyer_shifted'] = df.groupby(level=0)['beyer'].shift(1)

In [7]: df
                 beyer  line_race  beyer_shifted
Last Gunfighter     99         10            NaN
Last Gunfighter    102         10             99
Last Gunfighter    103          8            102
Paynter            103         10            NaN
Paynter             88         10            103
Paynter            100          8             88


Between times

Using indexer between time

Constructing a datetime range that excludes weekends and includes only certain times

Vectorized Lookup

Turn a matrix with hours in columns and days in rows into a continuous row sequence in the form of a time series. How to rearrange a python pandas DataFrame?

Dealing with duplicates when reindexing a timeseries to a specified frequency

Calculate the first day of the month for each entry in a DatetimeIndex

In [8]: dates = pd.date_range('2000-01-01', periods=5)

In [9]: dates.to_period(freq='M').to_timestamp()
<class 'pandas.tseries.index.DatetimeIndex'>
[2000-01-01, ..., 2000-01-01]
Length: 5, Freq: None, Timezone: None


The Plotting docs.

Make Matplotlib look like R

Setting x-axis major and minor labels

Plotting multiple charts in an ipython notebook

Creating a multi-line plot

Plotting a heatmap

Annotate a time-series plot

Annotate a time-series plot #2

Generate Embedded plots in excel files using Pandas, Vincent and xlsxwriter

Boxplot for each quartile of a stratifying variable

In [10]: df = pd.DataFrame(
   ....:     {u'stratifying_var': np.random.uniform(0, 100, 20),
   ....:      u'price': np.random.normal(100, 5, 20)}
   ....: )

In [11]: df[u'quartiles'] = pd.qcut(
   ....:     df[u'stratifying_var'],
   ....:     4,
   ....:     labels=[u'0-25%', u'25-50%', u'50-75%', u'75-100%']
   ....: )

In [12]: df.boxplot(column=u'price', by=u'quartiles')
Out[12]: <matplotlib.axes.AxesSubplot at 0xaa40108c>

Data In/Out

Performance comparison of SQL vs HDF5


The CSV docs

read_csv in action

appending to a csv

Reading a csv chunk-by-chunk

Reading only certain rows of a csv chunk-by-chunk

Reading the first few lines of a frame

Reading a file that is compressed but not by gzip/bz2 (the native compressed formats which read_csv understands). This example shows a WinZipped file, but is a general application of opening the file within a context manager and using that handle to read. See here

Inferring dtypes from a file

Dealing with bad lines

Dealing with bad lines II

Reading CSV with Unix timestamps and converting to local timezone

Write a multi-row index CSV without writing duplicates

Parsing date components in multi-columns is faster with a format

In [30]: i = pd.date_range('20000101',periods=10000)

In [31]: df = pd.DataFrame(dict(year = i.year, month = i.month, day =

In [32]: df.head()
   day  month  year
0    1      1  2000
1    2      1  2000
2    3      1  2000
3    4      1  2000
4    5      1  2000

In [33]: %timeit pd.to_datetime(df.year*10000+df.month*,format='%Y%m%d')
100 loops, best of 3: 7.08 ms per loop

# simulate combinging into a string, then parsing
In [34]: ds = df.apply(lambda x: "%04d%02d%02d" % (x['year'],x['month'],x['day']),axis=1)

In [35]: ds.head()
0    20000101
1    20000102
2    20000103
3    20000104
4    20000105
dtype: object

In [36]: %timeit pd.to_datetime(ds)
1 loops, best of 3: 488 ms per loop


The HDFStores docs

Simple Queries with a Timestamp Index

Managing heterogeneous data using a linked multiple table hierarchy

Merging on-disk tables with millions of rows

Deduplicating a large store by chunks, essentially a recursive reduction operation. Shows a function for taking in data from csv file and creating a store by chunks, with date parsing as well. See here

Creating a store chunk-by-chunk from a csv file

Appending to a store, while creating a unique index

Large Data work flows

Reading in a sequence of files, then providing a global unique index to a store while appending

Groupby on a HDFStore

Hierarchical queries on a HDFStore

Counting with a HDFStore

Troubleshoot HDFStore exceptions

Setting min_itemsize with strings

Using ptrepack to create a completely-sorted-index on a store

Storing Attributes to a group node

In [13]: df = DataFrame(np.random.randn(8,3))

In [14]: store = HDFStore('test.h5')

In [15]: store.put('df',df)

# you can store an arbitrary python object via pickle
In [16]: store.get_storer('df').attrs.my_attribute = dict(A = 10)

In [17]: store.get_storer('df').attrs.my_attribute
Out[17]: {'A': 10}

Binary Files

pandas readily accepts numpy record arrays, if you need to read in a binary file consisting of an array of C structs. For example, given this C program in a file called main.c compiled with gcc main.c -std=gnu99 on a 64-bit machine,

#include <stdio.h>
#include <stdint.h>

typedef struct _Data
    int32_t count;
    double avg;
    float scale;
} Data;

int main(int argc, const char *argv[])
    size_t n = 10;
    Data d[n];

    for (int i = 0; i < n; ++i)
        d[i].count = i;
        d[i].avg = i + 1.0;
        d[i].scale = (float) i + 2.0f;

    FILE *file = fopen("binary.dat", "wb");
    fwrite(&d, sizeof(Data), n, file);

    return 0;

the following Python code will read the binary file 'binary.dat' into a pandas DataFrame, where each element of the struct corresponds to a column in the frame:

import numpy as np
from pandas import DataFrame

names = 'count', 'avg', 'scale'

# note that the offsets are larger than the size of the type because of
# struct padding
offsets = 0, 8, 16
formats = 'i4', 'f8', 'f4'
dt = np.dtype({'names': names, 'offsets': offsets, 'formats': formats},
df = DataFrame(np.fromfile('binary.dat', dt))


The offsets of the structure elements may be different depending on the architecture of the machine on which the file was created. Using a raw binary file format like this for general data storage is not recommended, as it is not cross platform. We recommended either HDF5 or msgpack, both of which are supported by pandas’ IO facilities.

Aliasing Axis Names

To globally provide aliases for axis names, one can define these 2 functions:

In [18]: def set_axis_alias(cls, axis, alias):
   ....:      if axis not in cls._AXIS_NUMBERS:
   ....:          raise Exception("invalid axis [%s] for alias [%s]" % (axis, alias))
   ....:      cls._AXIS_ALIASES[alias] = axis
In [19]: def clear_axis_alias(cls, axis, alias):
   ....:      if axis not in cls._AXIS_NUMBERS:
   ....:          raise Exception("invalid axis [%s] for alias [%s]" % (axis, alias))
   ....:      cls._AXIS_ALIASES.pop(alias,None)
In [20]: set_axis_alias(DataFrame,'columns', 'myaxis2')

In [21]: df2 = DataFrame(randn(3,2),columns=['c1','c2'],index=['i1','i2','i3'])

In [22]: df2.sum(axis='myaxis2')
i1   -1.335466
i2   -1.032281
i3   -0.488638
dtype: float64

In [23]: clear_axis_alias(DataFrame,'columns', 'myaxis2')

Creating Example Data

To create a dataframe from every combination of some given values, like R’s expand.grid() function, we can create a dict where the keys are column names and the values are lists of the data values:

In [24]: import itertools

In [25]: def expand_grid(data_dict):
   ....:     rows = itertools.product(*data_dict.values())
   ....:     return pd.DataFrame.from_records(rows, columns=data_dict.keys())

In [26]: df = expand_grid(
   ....:     {'height': [60, 70],
   ....:      'weight': [100, 140, 180],
   ....:      'sex': ['Male', 'Female']}
   ....: )

In [27]: df
       sex  weight  height
0     Male     100      60
1     Male     100      70
2     Male     140      60
3     Male     140      70
4     Male     180      60
5     Male     180      70
6   Female     100      60
7   Female     100      70
8   Female     140      60
9   Female     140      70
10  Female     180      60
11  Female     180      70