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Pandas Cheat Sheet Rev2 Cheat Sheet by

Pandas Cheat SheetExtra text to meet the character requirements

Import Data

df = pd.rea­d_c­sv­­('f­­il­e­n­am­­e.csv')
Read CSV into a Pandas DataFrame
df = pd.to_­csv­('f­­il­e­n­am­­e.csv')
Export Pandas DataFrame to CSV
Import Options:

header­=False, Index=­False, usecol­s=(5,6)

Can also read CSV / HTML / Excel / JSON

Combine multiple files into one (1) DataFrame

all_files = glob.glob(*/.txt')
Finds all txt files in the
df_raw = [pd.re­ad_­csv(f) for f in all_files]
Makes multiple DataFrames
df_all = pd.con­cat­(df­_raw, ignore­_in­dex­=True
Concat­enates all the DataFrames into one (1) large DataFrame

Select Data

df.head(5)
Reads the first 5 rows
df.tail(5)
Reads the last 5 rows
df.shape()
Gives the number of columns and rows in the DataFrame

Select Row

data.i­loc[0]
First row of DataFrame
data.i­loc[1]
Second row of DataFrame

Select Column

data.i­loc[:0]
First column of DataFrame
data.i­loc[:1]
Second column of DataFrame

Select Column and Row Combined

df.ilo­c[:3, :2]
Selecting first 3 rows and first 2 columns
df.ilo­c[:3, ['Colu­mn1', 'Colum­n2']]
Selecting first 3 rows and first 2 columns

Re-Order Colums

df = df[['C­olu­mn3', 'Column2', 'Colum­n1']]
Re-orders the columns to the order specified in this list
 

Drop Columns

df= df.dro­p(c­olu­mns­=['­Col­umn1'], axis=1)
Drops 'Colum­n1_­Name' from DataFrame

Sort Columns

df = df.sor­t_v­alu­es(­by=­['C­olu­mn1'], ascend­ing­=False)
Sort values by Column1

Filter Column

df= df[(df­­['­C­o­lu­­mn1'] >= some_n­­umber]
Filter DataFrame by certain value

Rename Columns

df.columns = ['A', 'B']
Renamed Columns to 'A' and 'B'

Merge DataFrames

df1.a­­ppe­­nd­(­df2)
Joins df1 and df2

Filter on Condition

df = df[(df> 2).all­(ax­is=1)]
Removes any values less than 2

Select Row Based on Condition

row = df[df.A > 3].iloc[0]
Select first row where A > 0

Dealing with NAN values

df= df.fil­lna­(me­tho­d='­ffill')
Fills blank values using forward fill method
df= df.fil­lna­(me­tho­d='­bfill')
Fills blank values using backwards fill method
df.dro­pna­(in­pla­ce=­True)
Removes rows with no values

Padding Values With Zero's

df['Co­lumn1'] = df['Co­lum­n1'­].a­sty­pe(­str­).s­tr.z­fi­ll(6)
Sets the numbe to six (6) long, which adds zeros

Change Column Data Type

df['Co­lum­n1']= df['Co­lum­n1'­].a­sty­pe(­float)
Change 'Column1' to float

Convert Column to Date / Time

df['Time'] = df['Ti­me'­].a­ppl­y(p­d.t­o_d­ate­time)
Converts the time column to a Datetime Series
 

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