Show Menu
Cheatography
  1. Home >
  2. Programming >

Pandas for Data Analysis Cheat Sheet (DRAFT) by

Consolidated list of commands for pandas library in Data Analysis.

This is a draft cheat sheet. It is a work in progress and is not finished yet.

Shared Attributes

.head(n)
.tail(n)
.index
.values
.shape
.axes
.info()
Common attributes shared between pd.Series and pd.Dat­aFrame

Null Unique Values

Drop Null Values
df.dro­pna­(how= any/all, inplace = T/F)

df.dro­pna­(subset = ['col_­name1', 'col_n­ame2'], how = any/all, inplace = T/F)
- from specific columns
Fill Null Values
df.fil­lna­(value, inplace = T/F)
- acts on entire data frame.
df['co­l_n­ame­'].f­il­lna­(value = 'fill_­value', inplace = T/F)
- act on selected columns
Unique Values
df['co­l].u­ni­que()
- Returns unique values as a list(i­ncl­uding null values)
df.nun­ique()
- No of unique Values.(by default doesn't count null)
df.uni­que­(dropna = False)

df['co­l'].nu­nique()

df['co­l'].un­iqu­e(d­ropna = True/F­alse)

Panda Series
pd.Ser­ies.ha­snans

pds.nu­nique()
- No of unique Values.
pdf.un­iqu­e(d­ropna = True/F­alse)
- Returns unique values in a list.

Dealing Columns

df.columns
-retuns column names as a list
df['ne­w_c­ol'­]=l­ist­/series

df.ins­ert[loc = n, column= col_name, value= New_va­lue­/array]
-inserts column at desired position / location

Sort, Rank, Count

Sort
df.sor­t_i­nde­x(a­sce­nding = True/F­alse)
- Sort Index
df.sor­t_v­alu­es(­col­_name, na_pos­ition = "­Fir­st/­Las­t", ascending = True/F­alse)
- Sort based on values
df.sor­t_v­alu­es(­['c­ol_­name, 'col2'], ascending = [True, False])
- Sort by Multiple Columns
Rank
df['co­l'].ra­nk(­asc­ending = True/F­alse)
- Ranks are assigned based on sorted values
counts
df.val­ue_­cou­nts()
- counts exact rows
df.val­ue_­cou­nts­(no­rmalise = True)

pds.va­lue­_co­unts()
- pd series

Data Type Conver­sions and Optimi­zation

df['col'] = pd.to_­dat­eti­me(­df[­'col'])

or we can parse dates in import itself.
pd.rea­d_c­sv(......, parse_­dates = ['col1­','­col2'])

df['col'] = df['co­l'].as­typ­e(d­type)

dtype = bool, category, int

Filtering Data

Multiple Condit­ional Filtering
mask1 = df[col1'] == 'value'
- Returns True/False boolean series
mask2 = df[col2'] <= 'value'

mask3 = df[col3'] >= 'value'

df[(mask1 & mask2) | mask3]

Inclusion Check
mask = df['co­l'].is­in(­['v­al1­','­val­2',­'va­l3'])
- Check for inclusion using isin() method.
mask1 = df[col1'] == 'val1'
- isin method is equal to three condit­ional checks. 
mask1 = df[col1'] == 'val2'

mask1 = df[col1'] == 'val3'

For NULL values
mask = df['co­l'].is­null()
- Returns True/False boolean series
mask = df['co­l'].no­tnull()
- Returns True/False boolean series
Inclusion Check within a range
mask = df['co­l'].be­twe­en(­val1, val2)
-Returns True/False boolean series. True for values within range.
Duplicate Values
mask = df['co­l'].du­pli­cat­ed(keep = "­Fir­st/­Las­t/F­als­e")
- Returns boolean series, True for Duplicates
df.dro­p_d­upl­ica­tes()
-Deletes duplicate from df. if applied on df complete row should be identical.
df.dro­p_d­upl­ica­tes­(subset = ['col1­','­col2'])
- Drops if the combin­ation of col1 and col2 are identical.

Data Extraction

SET INDEX
RESET INDEX
LOC ACCESSOR
ILOC ACCESSOR

CODE