pandas dataframe notes.pdf

Version 2 May 2015 - [Draft – Mark Graph – mark dot the dot graph at gmail dot com – @Mark_Graph on twitter]
1
Cheat Sheet: The pandas DataFrame Object
Preliminaries
Start by importing these Python modules
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from pandas import DataFrame, Series
Note: these are the recommended import aliases
The conceptual model
DataFrame object: The pandas DataFrame is a two-
dimensional table of data with column and row indexes.
The columns are made up of pandas Series objects.
Series object: an ordered, one-dimensional array of
data with an index. All the data in a Series is of the
same data type. Series arithmetic is vectorised after first
aligning the Series index for each of the operands.
s1 = Series(range(0,4)) # -> 0, 1, 2, 3
s2 = Series(range(1,5)) # -> 1, 2, 3, 4
s3 = s1 + s2 # -> 1, 3, 5, 7
s4 = Series(['a','b'])*3 # -> 'aaa','bbb'
The index object: The pandas Index provides the axis
labels for the Series and DataFrame objects. It can only
contain hashable objects. A pandas Series has one
Index; and a DataFrame has two Indexes.
# --- get Index from Series and DataFrame
idx = s.index
idx = df.columns # the column index
idx = df.index # the row index
# --- some Index attributes
b = idx.is_monotonic_decreasing
b = idx.is_monotonic_increasing
b = idx.has_duplicates
i = idx.nlevels # multi-level indexes
# --- some Index methods
a = idx.values() # get as numpy array
l = idx.tolist() # get as a python list
idx = idx.astype(dtype)# change data type
b = idx.equals(o) # check for equality
idx = idx.union(o) # union of two indexes
i = idx.nunique() # number unique labels
label = idx.min() # minimum label
label = idx.max() # maximum label
Get your data into a DataFrame
Load a DataFrame from a CSV file
df = pd.read_csv('file.csv')# often works
df = pd.read_csv(‘file.csv’, header=0,
index_col=0, quotechar=’”’,sep=’:’,
na_values = [‘na’, ‘-‘, ‘.’, ‘’])
Note: refer to pandas docs for all arguments
From inline CSV text to a DataFrame
from StringIO import StringIO # python2.7
#from io import StringIO # python 3
data = """, Animal, Cuteness, Desirable
row-1, dog, 8.7, True
row-2, bat, 2.6, False"""
df = pd.read_csv(StringIO(data),
header=0, index_col=0,
skipinitialspace=True)
Note: skipinitialspace=True allows a pretty layout
Load DataFrames from a Microsoft Excel file
# Each Excel sheet in a Python dictionary
workbook = pd.ExcelFile('file.xlsx')
dictionary = {}
for sheet_name in workbook.sheet_names:
df = workbook.parse(sheet_name)
dictionary[sheet_name] = df
Note: the parse() method takes many arguments like
read_csv() above. Refer to the pandas documentation.
Load a DataFrame from a MySQL database
import pymysql
from sqlalchemy import create_engine
engine = create_engine('mysql+pymysql://'
+'USER:PASSWORD@localhost/DATABASE')
df = pd.read_sql_table('table', engine)
Data in Series then combine into a DataFrame
# Example 1 ...
s1 = Series(range(6))
s2 = s1 * s1
s2.index = s2.index + 2# misalign indexes
df = pd.concat([s1, s2], axis=1)
# Example 2 ...
s3 = Series({'Tom':1, 'Dick':4, 'Har':9})
s4 = Series({'Tom':3, 'Dick':2, 'Mar':5})
df = pd.concat({'A':s3, 'B':s4 }, axis=1)
Note: 1st method has in integer column labels
Note: 2nd method does not guarantee col order
Note: index alignment on DataFrame creation
Get a DataFrame from data in a Python dictionary
# default --- assume data is in columns
df = DataFrame({
'col0' : [1.0, 2.0, 3.0, 4.0],
'col1' : [100, 200, 300, 400]
})
Column
index
(df.columns)

Series
of
data

Series
of
data

Series
of
data

Series
of
data

Series
of
data

Series
of
data

Series
of
data

Row
index

(df.index)

2
Get a DataFrame from data in a Python dictionary
# --- use helper method for data in rows
df = DataFrame.from_dict({ # data by row
'row0' : {'col0':0, 'col1':'A'},
'row1' : {'col0':1, 'col1':'B'}
}, orient='index')
df = DataFrame.from_dict({ # data by row
'row0' : [1, 1+1j, 'A'],
'row1' : [2, 2+2j, 'B']
}, orient='index')
Create play/fake data (useful for testing)
# --- simple
df = DataFrame(np.random.rand(50,5))
# --- with a time-stamp row index:
df.index = pd.date_range('1/1/2006',
periods=len(df), freq='M')
# --- with alphabetic row and col indexes
import string
import random
r = 52 # note: min r is 1; max r is 52
c = 5
df = DataFrame(np.random.randn(r, c),
columns = ['col'+str(i) for i in
range(c)],
index = list((string.uppercase +
string.lowercase)[0:r]))
df['group'] = list(
''.join(random.choice('abcd')
for _ in range(r))
)
Saving a DataFrame
Saving a DataFrame to a CSV file
df.to_csv('name.csv', encoding='utf-8')
Saving DataFrames to an Excel Workbook
from pandas import ExcelWriter
writer = ExcelWriter('filename.xlsx')
df1.to_excel(writer,'Sheet1')
df2.to_excel(writer,'Sheet2')
writer.save()
Saving a DataFrame to MySQL
import pymysql
from sqlalchemy import create_engine
e = create_engine('mysql+pymysql://' +
'USER:PASSWORD@localhost/DATABASE')
df.to_sql('TABLE',e, if_exists='replace')
Note: if_exists ! 'fail', 'replace', 'append'
Saving a DataFrame to a Python dictionary
dictionary = df.to_dict()
Saving a DataFrame to a Python string
string = df.to_string()
Note: sometimes may be useful for debugging
Working with the whole DataFrame
Peek at the DataFrame contents
df.info() # index & data types
n = 4
dfh = df.head(n) # get first n rows
dft = df.tail(n) # get last n rows
dfs = df.describe() # summary stats cols
top_left_corner_df = df.iloc[:5, :5]
DataFrame non-indexing attributes
dfT = df.T # transpose rows and cols
l = df.axes # list row and col indexes
(r, c) = df.axes # from above
s = df.dtypes # Series column data types
b = df.empty # True for empty DataFrame
i = df.ndim # number of axes (2)
t = df.shape # (row-count, column-count)
(r, c) = df.shape # from above
i = df.size # row-count * column-count
a = df.values # get a numpy array for df
DataFrame utility methods
dfc = df.copy() # copy a DataFrame
dfr = df.rank() # rank each col (default)
dfs = df.sort() # sort each col (default)
dfc = df.astype(dtype) # type conversion
DataFrame iteration methods
df.iteritems()# (col-index, Series) pairs
df.iterrows() # (row-index, Series) pairs
# example ... iterating over columns
for (name, series) in df.iteritems():
print('Col name: ' + str(name))
print('First value: ' +
str(series.iat[0]) + 'n')
Maths on the whole DataFrame (not a complete list)
df = df.abs() # absolute values
df = df.add(o) # add df, Series or value
s = df.count() # non NA/null values
df = df.cummax() # (cols default axis)
df = df.cummin() # (cols default axis)
df = df.cumsum() # (cols default axis)
df = df.cumprod() # (cols default axis)
df = df.diff() # 1st diff (col def axis)
df = df.div(o) # div by df, Series, value
df = df.dot(o) # matrix dot product
s = df.max() # max of axis (col def)
s = df.mean() # mean (col default axis)
s = df.median()# median (col default)
s = df.min() # min of axis (col def)
df = df.mul(o) # mul by df Series val
s = df.sum() # sum axis (cols default)
Note: The methods that return a series default to
working on columns.
DataFrame filter/select rows or cols on label info
df = df.filter(items=['a', 'b']) # by col
df = df.filter(items=[5], axis=0) #by row
df = df.filter(like='x') # keep x in col
df = df.filter(regex='x') # regex in col
df = df.select(crit=(lambda x:not x%5))#r
Note: select takes a Boolean function, for cols: axis=1
Note: filter defaults to cols; select defaults to rows

3
Working with Columns
A DataFrame column is a pandas Series object
Get column index and labels
idx = df.columns # get col index
label = df.columns[0] # 1st col label
lst = df.columns.tolist() # get as a list
Change column labels
df.rename(columns={'old':'new'},
inplace=True)
df = df.rename(columns={'a':1,'b':'x'})
Selecting columns
s = df['colName'] # select col to Series
df = df[['colName']] # select col to df
df = df[['a','b']] # select 2 or more
df = df[['c','a','b']]# change order
s = df[df.columns[0]] # select by number
df = df[df.columns[[0, 3, 4]] # by number
s = df.pop('c') # get col & drop from df
Selecting columns with Python attributes
s = df.a # same as s = df['a']
# cannot create new columns by attribute
df.existing_col = df.a / df.b
df['new_col'] = df.a / df.b
Trap: column names must be valid identifiers.
Adding new columns to a DataFrame
df['new_col'] = range(len(df))
df['new_col'] = np.repeat(np.nan,len(df))
df['random'] = np.random.rand(len(df))
df['index_as_col'] = df.index
df1[['b','c']] = df2[['e','f']]
df3 = df1.append(other=df2)
Trap: When adding an indexed pandas object as a new
column, only items from the new series that have a
corresponding index in the DataFrame will be added.
The receiving DataFrame is not extended to
accommodate the new series. To merge, see below.
Trap: when adding a python list or numpy array, the
column will be added by integer position.
Swap column contents – change column order
df[['B', 'A']] = df[['A', 'B']]
Dropping columns (mostly by label)
df = df.drop('col1', axis=1)
df.drop('col1', axis=1, inplace=True)
df = df.drop(['col1','col2'], axis=1)
s = df.pop('col') # drops from frame
del df['col'] # even classic python works
df.drop(df.columns[0], inplace=True)
Vectorised arithmetic on columns
df['proportion']=df['count']/df['total']
df['percent'] = df['proportion'] * 100.0
Apply numpy mathematical functions to columns
df['log_data'] = np.log(df['col1'])
df['rounded'] = np.round(df['col2'], 2)
Note: Many more mathematical functions
Columns value set based on criteria
df['b']=df['a'].where(df['a']>0,other=0)
df['d']=df['a'].where(df.b!=0,other=df.c)
Note: where other can be a Series or a scalar
Data type conversions
s = df['col'].astype(str) # Series dtype
na = df['col'].values # numpy array
pl = df['col'].tolist() # python list
Note: useful dtypes for Series conversion: int, float, str
Trap: index lost in conversion from Series to array or list
Common column-wide methods/attributes
value = df['col'].dtype # type of data
value = df['col'].size # col dimensions
value = df['col'].count()# non-NA count
value = df['col'].sum()
value = df['col'].prod()
value = df['col'].min()
value = df['col'].max()
value = df['col'].mean()
value = df['col'].median()
value = df['col'].cov(df['col2'])
s = df['col'].describe()
s = df['col'].value_counts()
Find index label for min/max values in column
label = df['col1'].idxmin()
label = df['col1'].idxmax()
Common column element-wise methods
s = df['col'].isnull()
s = df['col'].notnull() # not isnull()
s = df['col'].astype(float)
s = df['col'].round(decimals=0)
s = df['col'].diff(periods=1)
s = df['col'].shift(periods=1)
s = df['col'].to_datetime()
s = df['col'].fillna(0) # replace NaN w 0
s = df['col'].cumsum()
s = df['col'].cumprod()
s = df['col'].pct_change(periods=4)
s = df['col'].rolling_sum(periods=4,
window=4)
Note: also rolling_min(), rolling_max(), and many more.
Append a column of row sums to a DataFrame
df['Total'] = df.sum(axis=1)
Note: also means, mins, maxs, etc.
Multiply every column in DataFrame by Series
df = df.mul(s, axis=0) # on matched rows
Note: also add, sub, div, etc.
Selecting columns with .loc, .iloc and .ix
df = df.loc[:, 'col1':'col2'] # inclusive
df = df.iloc[:, 0:2] # exclusive
Get the integer position of a column index label
j = df.columns.get_loc('col_name')
Test if column index values are unique/monotonic
if df.columns.is_unique: pass # ...
b = df.columns.is_monotonic_increasing
b = df.columns.is_monotonic_decreasing

4
Working with rows
Get the row index and labels
idx = df.index # get row index
label = df.index[0] # 1st row label
lst = df.index.tolist() # get as a list
Change the (row) index
df.index = idx # new ad hoc index
df.index = range(len(df)) # set with list
df = df.reset_index() # replace old w new
# note: old index stored as a col in df
df = df.reindex(index=range(len(df)))
df = df.set_index(keys=['r1','r2','etc'])
df.rename(index={'old':'new'},
inplace=True)
Adding rows
df = original_df.append(more_rows_in_df)
Hint: convert to a DataFrame and then append. Both
DataFrames should have same column labels.
Dropping rows (by name)
df = df.drop('row_label')
df = df.drop(['row1','row2']) # multi-row
Boolean row selection by values in a column
df = df[df['col2'] >= 0.0]
df = df[(df['col3']>=1.0) |
(df['col1']<0.0)]
df = df[df['col'].isin([1,2,5,7,11])]
df = df[~df['col'].isin([1,2,5,7,11])]
df = df[df['col'].str.contains('hello')]
Trap: bitwise "or", "and" “not” (ie. | & ~) co-opted to be
Boolean operators on a Series of Boolean
Trap: need parentheses around comparisons.
Selecting rows using isin over multiple columns
# fake up some data
data = {1:[1,2,3], 2:[1,4,9], 3:[1,8,27]}
df = pd.DataFrame(data)
# multi-column isin
lf = {1:[1, 3], 3:[8, 27]} # look for
f = df[df[list(lf)].isin(lf).all(axis=1)]
Selecting rows using an index
idx = df[df['col'] >= 2].index
print(df.ix[idx])
Select a slice of rows by integer position
[inclusive-from : exclusive-to [: step]]
default start is 0; default end is len(df)
df = df[:] # copy DataFrame
df = df[0:2] # rows 0 and 1
df = df[-1:] # the last row
df = df[2:3] # row 2 (the third row)
df = df[:-1] # all but the last row
df = df[::2] # every 2nd row (0 2 ..)
Trap: a single integer without a colon is a column label
for integer numbered columns.
Select a slice of rows by label/index
[inclusive-from : inclusive–to [ : step]]
df = df['a':'c'] # rows 'a' through 'c'
Trap: doesn't work on integer labelled rows
Append a row of column totals to a DataFrame
# Option 1: use dictionary comprehension
sums = {col: df[col].sum() for col in df}
sums_df = DataFrame(sums,index=['Total'])
df = df.append(sums_df)
# Option 2: All done with pandas
df = df.append(DataFrame(df.sum(),
columns=['Total']).T)
Iterating over DataFrame rows
for (index, row) in df.iterrows(): # pass
Trap: row data type may be coerced.
Sorting DataFrame rows values
df = df.sort(df.columns[0],
ascending=False)
df.sort(['col1', 'col2'], inplace=True)
Random selection of rows
import random as r
k = 20 # pick a number
selection = r.sample(range(len(df)), k)
df_sample = df.iloc[selection, :]
Note: this sample is not sorted
Sort DataFrame by its row index
df.sort_index(inplace=True) # sort by row
df = df.sort_index(ascending=False)
Drop duplicates in the row index
df['index'] = df.index # 1 create new col
df = df.drop_duplicates(cols='index',
take_last=True)# 2 use new col
del df['index'] # 3 del the col
df.sort_index(inplace=True)# 4 tidy up
Test if two DataFrames have same row index
len(a)==len(b) and all(a.index==b.index)
Get the integer position of a row or col index label
i = df.index.get_loc('row_label')
Trap: index.get_loc() returns an integer for a unique
match. If not a unique match, may return a slice or
mask.
Get integer position of rows that meet condition
a = np.where(df['col'] >= 2) #numpy array
Test if the row index values are unique/monotonic
if df.index.is_unique: pass # ...
b = df.index.is_monotonic_increasing
b = df.index.is_monotonic_decreasing

5
Working with cells
Selecting a cell by row and column labels
value = df.at['row', 'col']
value = df.loc['row', 'col']
value = df['col'].at['row'] # tricky
Note: .at[] fastest label based scalar lookup
Setting a cell by row and column labels
df.at['row, 'col'] = value
df.loc['row, 'col'] = value
df['col'].at['row'] = value # tricky
Selecting and slicing on labels
df = df.loc['row1':'row3', 'col1':'col3']
Note: the "to" on this slice is inclusive.
Setting a cross-section by labels
df.loc['A':'C', 'col1':'col3'] = np.nan
df.loc[1:2,'col1':'col2']=np.zeros((2,2))
df.loc[1:2,'A':'C']=othr.loc[1:2,'A':'C']
Remember: inclusive "to" in the slice
Selecting a cell by integer position
value = df.iat[9, 3] # [row, col]
value = df.iloc[0, 0] # [row, col]
value = df.iloc[len(df)-1,
len(df.columns)-1]
Selecting a range of cells by int position
df = df.iloc[2:4, 2:4] # subset of the df
df = df.iloc[:5, :5] # top left corner
s = df.iloc[5, :] # returns row as Series
df = df.iloc[5:6, :] # returns row as row
Note: exclusive "to" – same as python list slicing.
Setting cell by integer position
df.iloc[0, 0] = value # [row, col]
df.iat[7, 8] = value
Setting cell range by integer position
df.iloc[0:3, 0:5] = value
df.iloc[1:3, 1:4] = np.ones((2, 3))
df.iloc[1:3, 1:4] = np.zeros((2, 3))
df.iloc[1:3, 1:4] = np.array([[1, 1, 1],
[2, 2, 2]])
Remember: exclusive-to in the slice
.ix for mixed label and integer position indexing
value = df.ix[5, 'col1']
df = df.ix[1:5, 'col1':'col3']
Views and copies
From the manual: Setting a copy can cause subtle
errors. The rules about when a view on the data is
returned are dependent on NumPy. Whenever an array
of labels or a Boolean vector are involved in the indexing
operation, the result will be a copy.
In summary: indexes and addresses
In the main, these notes focus on the simple, single
level Indexes. Pandas also has a hierarchical or
multi-level Indexes (aka the MultiIndex).
A DataFrame has two Indexes
• Typically, the column index (df.columns) is a list of
strings (observed variable names) or (less
commonly) integers (the default is numbered from 0
to length-1)
• Typically, the row index (df.index) might be:
o Integers - for case or row numbers (default is
numbered from 0 to length-1);
o Strings – for case names; or
o DatetimeIndex or PeriodIndex – for time series
data (more below)
Indexing
# --- selecting columns
s = df['col_label'] # scalar
df = df[['col_label']] # one item list
df = df[['L1', 'L2']] # many item list
df = df[index] # pandas Index
df = df[s] # pandas Series
# --- selecting rows
df = df['from':'inc_to']# label slice
df = df[3:7] # integer slice
df = df[df['col'] > 0.5]# Boolean Series
df = df.loc['label'] # single label
df = df.loc[container] # lab list/Series
df = df.loc['from':'to']# inclusive slice
df = df.loc[bs] # Boolean Series
df = df.iloc[0] # single integer
df = df.iloc[container] # int list/Series
df = df.iloc[0:5] # exclusive slice
df = df.ix[x] # loc then iloc
# --- select DataFrame cross-section
# r and c can be scalar, list, slice
df.loc[r, c] # label accessor (row, col)
df.iloc[r, c]# integer accessor
df.ix[r, c] # label access int fallback
df[c].iloc[r]# chained – also for .loc
# --- select cell
# r and c must be label or integer
df.at[r, c] # fast scalar label accessor
df.iat[r, c] # fast scalar int accessor
df[c].iat[r] # chained – also for .at
# --- indexing methods
v = df.get_value(r, c) # get by row, col
df = df.set_value(r,c,v)# set by row, col
df = df.xs(key, axis) # get cross-section
df = df.filter(items, like, regex, axis)
df = df.select(crit, axis)
Note: the indexing attributes (.loc, .iloc, .ix, .at .iat) can
be used to get and set values in the DataFrame.
Note: the .loc, iloc and .ix indexing attributes can accept
python slice objects. But .at and .iat do not.
Note: .loc can also accept Boolean Series arguments
Avoid: chaining in the form df[col_indexer][row_indexer]
Trap: label slices are inclusive, integer slices exclusive.

6
Joining/Combining DataFrames
Three ways to join two DataFrames:
• merge (a database/SQL-like join operation)
• concat (stack side by side or one on top of the other)
• combine_first (splice the two together, choosing
values from one over the other)
Merge on indexes
df_new = pd.merge(left=df1, right=df2,
how='outer', left_index=True,
right_index=True)
How: 'left', 'right', 'outer', 'inner'
How: outer=union/all; inner=intersection
Merge on columns
df_new = pd.merge(left=df1, right=df2,
how='left', left_on='col1',
right_on='col2')
Trap: When joining on columns, the indexes on the
passed DataFrames are ignored.
Trap: many-to-many merges on a column can result in
an explosion of associated data.
Join on indexes (another way of merging)
df_new = df1.join(other=df2, on='col1',
how='outer')
df_new = df1.join(other=df2,on=['a','b'],
how='outer')
Note: DataFrame.join() joins on indexes by default.
DataFrame.merge() joins on common columns by
default.
Simple concatenation is often the best
df=pd.concat([df1,df2],axis=0)#top/bottom
df = df1.append([df2, df3]) #top/bottom
df=pd.concat([df1,df2],axis=1)#left/right
Trap: can end up with duplicate rows or cols
Note: concat has an ignore_index parameter
Combine_first
df = df1.combine_first(other=df2)
# multi-combine with python reduce()
df = reduce(lambda x, y:
x.combine_first(y),
[df1, df2, df3, df4, df5])
Uses the non-null values from df1. The index of the
combined DataFrame will be the union of the indexes
from df1 and df2.
Groupby: Split-Apply-Combine
The pandas "groupby" mechanism allows us to split the
data into groups, apply a function to each group
independently and then combine the results.
Grouping
gb = df.groupby('cat') # by one columns
gb = df.groupby(['c1','c2']) # by 2 cols
gb = df.groupby(level=0) # multi-index gb
gb = df.groupby(level=['a','b']) # mi gb
print(gb.groups)
Note: groupby() returns a pandas groupby object
Note: the groupby object attribute .groups contains a
dictionary mapping of the groups.
Trap: NaN values in the group key are automatically
dropped – there will never be a NA group.
Iterating groups – usually not needed
for name, group in gb:
print (name)
print (group)
Selecting a group
dfa = df.groupby('cat').get_group('a')
dfb = df.groupby('cat').get_group('b')
Applying an aggregating function
# apply to a column ...
s = df.groupby('cat')['col1'].sum()
s = df.groupby('cat')['col1'].agg(np.sum)
# apply to the every column in DataFrame
s = df.groupby('cat').agg(np.sum)
df_summary = df.groupby('cat').describe()
df_row_1s = df.groupby('cat').head(1)
Note: aggregating functions reduce the dimension by
one – they include: mean, sum, size, count, std, var,
sem, describe, first, last, min, max
Applying multiple aggregating functions
gb = df.groupby('cat')
# apply multiple functions to one column
dfx = gb['col2'].agg([np.sum, np.mean])
# apply to multiple fns to multiple cols
dfy = gb.agg({
'cat': np.count_nonzero,
'col1': [np.sum, np.mean, np.std],
'col2': [np.min, np.max]
})
Note: gb['col2'] above is shorthand for
df.groupby('cat')['col2'], without the need for regrouping.
Transforming functions
# transform to group z-scores, which have
# a group mean of 0, and a std dev of 1.
zscore = lambda x: (x-x.mean())/x.std()
dfz = df.groupby('cat').transform(zscore)
# replace missing data with group mean
mean_r = lambda x: x.fillna(x.mean())
dfm = df.groupby('cat').transform(mean_r)
Note: can apply multiple transforming functions in a
manner similar to multiple aggregating functions above,

7
Applying filtering functions
Filtering functions allow you to make selections based
on whether each group meets specified criteria
# select groups with more than 10 members
eleven = lambda x: (len(x['col1']) >= 11)
df11 = df.groupby('cat').filter(eleven)
Group by a row index (non-hierarchical index)
df = df.set_index(keys='cat')
s = df.groupby(level=0)['col1'].sum()
dfg = df.groupby(level=0).sum()
Pivot Tables
Pivot
Pivot tables move from long format to wide format data
df.columns = ['data'] # rename col
df.index = pd.period_range('3/3/2014',
df['year'] = df.index.year
df['month'] = df.index.month
# pivot to wide format
df = df.pivot(index='year',
columns='month', values='data')
# melt to long format
dfm = df
dfm['year'] = dfm.index
dfm = pd.melt(df, id_vars=['year'],
var_name='month', value_name='data')
# unstack to long format
# reset index to remove multi-level index
dfu=df.unstack().reset_index(name='data')
Value counts
s = df['col1'].value_counts()
Working with dates, times and their indexes
Dates and time – points and spans
With its focus on time-series data, pandas has a suite of
tools for managing dates and time: either as a point in
time (a Timestamp) or as a span of time (a Period).
t = pd.Timestamp('2013-01-01')
t = pd.Timestamp('2013-01-01 21:15:06')
t = pd.Timestamp('2013-01-01 21:15:06.7')
p = pd.Period('2013-01-01', freq='M')
Note: Timestamps should be in range 1678 and 2261
years. (Check Timestamp.max and Timestamp.min).
A Series of Timestamps or Periods
ts = ['2015-04-01 13:17:27',
'2014-04-02 13:17:29']
# Series of Timestamps (good)
s = pd.to_datetime(pd.Series(ts))
# Series of Periods (often not so good)
s = pd.Series( [pd.Period(x, freq='M')
for x in ts] )
s = pd.Series(
pd.PeriodIndex(ts,freq='S'))
Note: While Periods make a very useful index; they may
be less useful in a Series.
From non-standard strings to Timestamps
t = ['09:08:55.7654-JAN092002',
'15:42:02.6589-FEB082016']
s = pd.Series(pd.to_datetime(t,
format="%H:%M:%S.%f-%b%d%Y"))
Also: %B = full month name; %m = numeric month;
%y = year without century; and more …
Dates and time – stamps and spans as indexes
An index of Timestamps is a DatetimeIndex.
An index of Periods is a PeriodIndex.
date_strs = ['2014-01-01', '2014-04-01',
'2014-07-01', '2014-10-01']
dti = pd.DatetimeIndex(date_strs)
pid = pd.PeriodIndex(date_strs, freq='D')
pim = pd.PeriodIndex(date_strs, freq='M')
piq = pd.PeriodIndex(date_strs, freq='Q')
print (pid[1] - pid[0]) # 90 days
print (pim[1] - pim[0]) # 3 months
print (piq[1] - piq[0]) # 1 quarter
time_strs = ['2015-01-01 02:10:40.12345',
'2015-01-01 02:10:50.67890']
pis = pd.PeriodIndex(time_strs, freq='U')
df.index = pd.period_range('2015-01',
dti = pd.to_datetime(['04-01-2012'],
dayfirst=True) # Australian date format
pi = pd.period_range('1960-01-01',
'2015-12-31', freq='M')
Hint: unless you are working in less than seconds,
prefer PeriodIndex over DateTimeImdex.

8
Period frequency constants (not a complete list)
Name Description
U Microsecond
L Millisecond
S Second
T Minute
H Hour
D Calendar day
B Business day
W-{MON, TUE, …} Week ending on …
MS Calendar start of month
M Calendar end of month
QS-{JAN, FEB, …} Quarter start with year starting
(QS – December)
Q-{JAN, FEB, …} Quarter end with year ending (Q
– December)
AS-{JAN, FEB, …} Year start (AS - December)
A-{JAN, FEB, …} Year end (A - December)
From DatetimeIndex to Python datetime objects
dti = pd.DatetimeIndex(pd.date_range(
start='1/1/2011', periods=4, freq='M'))
s = Series([1,2,3,4], index=dti)
na = dti.to_pydatetime() #numpy array
na = s.index.to_pydatetime() #numpy array
Frome Timestamps to Python dates or times
df['date'] = [x.date() for x in df['TS']]
df['time'] = [x.time() for x in df['TS']]
Note: converts to datatime.date or datetime.time. But
does not convert to datetime.datetime.
From DatetimeIndex to PeriodIndex and back
df = DataFrame(np.random.randn(20,3))
df.index = pd.date_range('2015-01-01',
dfp = df.to_period(freq='M')
dft = dfp.to_timestamp()
Note: from period to timestamp defaults to the point in
time at the start of the period.
Working with a PeriodIndex
pi = pd.period_range('1960-01','2015-12',
freq='M')
na = pi.values # numpy array of integers
lp = pi.tolist() # python list of Periods
sp = Series(pi)# pandas Series of Periods
ss = Series(pi).astype(str) # S of strs
ls = Series(pi).astype(str).tolist()
Get a range of Timestamps
dr = pd.date_range('2013-01-01',
'2013-12-31', freq='D')
Error handling with dates
# 1st
example returns string not Timestamp
t = pd.to_datetime('2014-02-30')
# 2nd
example returns NaT (not a time)
t = pd.to_datetime('2014-02-30',
coerce=True)
# NaT like NaN tests True for isnull()
b = pd.isnull(t) # --> True
The tail of a time-series DataFrame
df = df.last("5M") # the last five months
Upsampling and downsampling
# upsample from quarterly to monthly
pi = pd.period_range('1960Q1',
periods=220, freq='Q')
df = DataFrame(np.random.rand(len(pi),5),
index=pi)
dfm = df.resample('M', convention='end')
# use ffill or bfill to fill with values
# downsample from monthly to quarterly
dfq = dfm.resample('Q', how='sum')
Time zones
t = ['2015-06-30 00:00:00',
'2015-12-31 00:00:00']
dti = pd.to_datetime(t
).tz_localize('Australia/Canberra')
dti = dti.tz_convert('UTC')
ts = pd.Timestamp('now',
tz='Europe/London')
# get a list of all time zones
import pyzt
for tz in pytz.all_timezones:
print tz
Note: by default, Timestamps are created without time
zone information.
Row selection with a time-series index
# start with the play data above
idx = pd.period_range('2015-01',
df.index = idx
february_selector = (df.index.month == 2)
february_data = df[february_selector]
q1_data = df[(df.index.month >= 1) &
(df.index.month <= 3)]
mayornov_data = df[(df.index.month == 5)
| (df.index.month == 11)]
totals = df.groupby(df.index.year).sum()
Also: year, month, day [of month], hour, minute, second,
dayofweek [Mon=0 .. Sun=6], weekofmonth, weekofyear
[numbered from 1], week starts on Monday], dayofyear
[from 1], …
The Series.dt accessor attribute
DataFrame columns that contain datetime-like objects
can be manipulated with the .dt accessor attribute
t = ['2012-04-14 04:06:56.307000',
'2011-05-14 06:14:24.457000',
'2010-06-14 08:23:07.520000']
# a Series of time stamps
s = pd.Series(pd.to_datetime(t))
print(s.dtype) # datetime64[ns]
print(s.dt.second) # 56, 24, 7
print(s.dt.month) # 4, 5, 6
# a Series of time periods
s = pd.Series(pd.PeriodIndex(t,freq='Q'))
print(s.dtype) # datetime64[ns]
print(s.dt.quarter) # 2, 2, 2
print(s.dt.year) # 2012, 2011, 2010

9
Working with missing and non-finite data
Working with missing data
Pandas uses the not-a-number construct (np.nan and
float('nan')) to indicate missing data. The Python None
can arise in data as well. It is also treated as missing
data; as is the pandas not-a-time construct
(pandas.NaT).
Missing data in a Series
s = Series( [8,None,float('nan'),np.nan])
#[8, NaN, NaN, NaN]
s.isnull() #[False, True, True, True]
s.notnull()#[True, False, False, False]
s.fillna(0)#[8, 0, 0, 0]
Missing data in a DataFrame
df = df.dropna() # drop all rows with NaN
df = df.dropna(axis=1) # same for cols
df=df.dropna(how='all') #drop all NaN row
df=df.dropna(thresh=2) # drop 2+ NaN in r
# only drop row if NaN in a specified col
df = df.dropna(df['col'].notnull())
Recoding missing data
df.fillna(0, inplace=True) # np.nan ! 0
s = df['col'].fillna(0) # np.nan ! 0
df = df.replace(r's+', np.nan,
regex=True) # white space ! np.nan
Non-finite numbers
With floating point numbers, pandas provides for
positive and negative infinity.
s = Series([float('inf'), float('-inf'),
np.inf, -np.inf])
Pandas treats integer comparisons with plus or minus
infinity as expected.
Testing for finite numbers
(using the data from the previous example)
b = np.isfinite(s)
Working with Categorical Data
Categorical data
The pandas Series has an R factors-like data type for
encoding categorical data.
s = Series(['a','b','a','c','b','d','a'],
dtype='category')
df['B'] = df['A'].astype('category')
Note: the key here is to specify the "category" data type.
Note: categories will be ordered on creation if they are
sortable. This can be turned off. See ordering below.
Convert back to the original data type
s = Series(['a','b','a','c','b','d','a'],
dtype='category')
s = s.astype('string')
Ordering, reordering and sorting
s = Series(list('abc'), dtype='category')
print (s.cat.ordered)
s=s.cat.reorder_categories(['b','c','a'])
s = s.sort()
s.cat.ordered = False
Trap: category must be ordered for it to be sorted
Renaming categories
s = Series(list('abc'), dtype='category')
s.cat.categories = [1, 2, 3] # in place
s = s.cat.rename_categories([4,5,6])
# using a comprehension ...
s.cat.categories = ['Group ' + str(i)
for i in s.cat.categories]
Trap: categories must be uniquely named
Adding new categories
s = s.cat.add_categories([4])
Removing categories
s = s.cat.remove_categories([4])
s.cat.remove_unused_categories() #inplace

10
Working with strings
Working with strings
# assume that df['col'] is series of
strings
s = df['col'].str.lower()
s = df['col'].str.upper()
s = df['col'].str.len()
# the next set work like Python
df['col'] += 'suffix' # append
df['col'] *= 2 # duplicate
s = df['col1'] + df['col2'] # concatenate
Most python string functions are replicated in the pandas
DataFrame and Series objects.
Regular expressions
s = df['col'].str.contains('regex')
s = df['col'].str.startswith('regex')
s = df['col'].str.endswith('regex')
s = df['col'].str.replace('old', 'new')
df['b'] = df.a.str.extract('(pattern)')
Note: pandas has many more regex methods.
Basic Statistics
Summary statistics
s = df['col1'].describe()
df1 = df.describe()
DataFrame – key stats methods
df.corr() # pairwise correlation cols
df.cov() # pairwise covariance cols
df.kurt() # kurtosis over cols (def)
df.mad() # mean absolute deviation
df.sem() # standard error of mean
df.var() # variance over cols (def)
Value counts
s = df['col1'].value_counts()
Cross-tabulation (frequency count)
ct = pd.crosstab(index=df['a'],
cols=df['b'])
Quantiles and ranking
quants = [0.05, 0.25, 0.5, 0.75, 0.95]
q = df.quantile(quants)
r = df.rank()
Histogram binning
count, bins = np.histogram(df['col1'])
count, bins = np.histogram(df['col'],
bins=5)
count, bins = np.histogram(df['col1'],
bins=[-3,-2,-1,0,1,2,3,4])
Regression
import statsmodels.formula.api as sm
result = sm.ols(formula="col1 ~ col2 +
col3", data=df).fit()
print (result.params)
print (result.summary())
Smoothing example using rolling_apply
k3x5 = np.array([1,2,3,3,3,2,1]) / 15.0
s = pd.rolling_apply(df['col1'],
window=7,
func=lambda x: (x * k3x5).sum(),
min_periods=7, center=True)
Cautionary note
This cheat sheet was cobbled together by bots roaming
the dark recesses of the Internet seeking ursine and
pythonic myths. There is no guarantee the narratives
were captured and transcribed accurately. You use
these notes at your own risk. You have been warned.

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