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)'
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