F M A
Data Wrangling
with pandas
Cheat Sheet
http://pandas.pydata.org
Syntax – Creating DataFrames
Tidy Data – A foundation for wrangling in pandas
In a tidy
data set:
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Each variable is saved
in its own column
&
Each observation is
saved in its own row
Tidy data complements pandas’s vectorized
operations. pandas will automatically preserve
observations as you manipulate variables. No
other format works as intuitively with pandas.
Reshaping Data – Change the layout of a data set
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*
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*
pd.melt(df)
Gather columns into rows.
df.pivot(columns='var', values='val')
Spread rows into columns.
pd.concat([df1,df2])
Append rows of DataFrames
pd.concat([df1,df2], axis=1)
Append columns of DataFrames
df.sort_values('mpg')
Order rows by values of a column (low to high).
df.sort_values('mpg',ascending=False)
Order rows by values of a column (high to low).
df.rename(columns = {'y':'year'})
Rename the columns of a DataFrame
df.sort_index()
Sort the index of a DataFrame
df.reset_index()
Reset index of DataFrame to row numbers, moving
index to columns.
df.drop(columns=['Length','Height'])
Drop columns from DataFrame
Subset Observations (Rows) Subset Variables (Columns)
a
b
c
1
4
7
10
2
5
8
11
3
6
9
12
df = pd.DataFrame(
{"a" : [4 ,5, 6],
"b" : [7, 8, 9],
"c" : [10, 11, 12]},
index = [1, 2, 3])
Specify values for each column.
df = pd.DataFrame(
[[4, 7, 10],
[5, 8, 11],
[6, 9, 12]],
index=[1, 2, 3],
columns=['a', 'b', 'c'])
Specify values for each row.
a
b
c
n
v
d
1
4
7
10
2
5
8
11
e
2
6
9
12
df = pd.DataFrame(
{"a" : [4 ,5, 6],
"b" : [7, 8, 9],
"c" : [10, 11, 12]},
index = pd.MultiIndex.from_tuples(
[('d',1),('d',2),('e',2)],
names=['n','v'])))
Create DataFrame with a MultiIndex
Method Chaining
Most pandas methods return a DataFrame so that
another pandas method can be applied to the
result. This improves readability of code.
df = (pd.melt(df)
.rename(columns={
'variable' : 'var',
'value' : 'val'})
.query('val >= 200')
)
df[df.Length > 7]
Extract rows that meet logical
criteria.
df.drop_duplicates()
Remove duplicate rows (only
considers columns).
df.head(n)
Select first n rows.
df.tail(n)
Select last n rows.
Logic in Python (and pandas)
<
Less than
!=
Not equal to
>
Greater than
df.column.isin
(values)
Group membership
==
Equals
pd.isnull
(obj)
Is
NaN
<=
Less than or equals
pd.notnull
(obj)
Is not
NaN
>=
Greater than or equals
&,|,~,^,
df.any(),df.all()
Logical
and, or, not, xor, any, all
http://pandas.pydata.org/ This cheat sheet inspired by Rstudio Data Wrangling Cheatsheet (https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf) Written by Irv Lustig, Princeton Consultants
df[['width','length','species']]
Select multiple columns with specific names.
df['width'] or df.width
Select single column with specific name.
df.filter(regex='regex')
Select columns whose name matches regular expression regex.
df.loc[:,'x2':'x4']
Select all columns between x2 and x4 (inclusive).
df.iloc[:,[1,2,5]]
Select columns in positions 1, 2 and 5 (first column is 0).
df.loc[df['a'] > 10, ['a','c']]
Select rows meeting logical condition, and only the specific columns .
regex (Regular Expressions) Examples
'
\.'
Matches strings containing
a period '.'
'Length$'
Matches strings ending with word 'Length'
'^Sepal'
Matches strings beginning with the word 'Sepal'
'^x[1
-5]$'
Matches strings beginning with 'x' and ending with 1,2,3,4,5
''^(?!Species$).*'
Matches strings except
the string 'Species'
df.sample(frac=0.5)
Randomly select fraction of rows.
df.sample(n=10)
Randomly select n rows.
df.iloc[10:20]
Select rows by position.
df.nlargest(n, 'value')
Select and order top n entries.
df.nsmallest(n, 'value')
Select and order bottom n entries.
