Pandas Cheat Sheet Rev2 Cheat Sheet

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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