Pandas¶

Tutorials¶

Parse Excel or CSV into DataFrame¶

import pandas as pd
# data = pd.read_excel('example.xlsx', 'sheet1')
data = pd.read_csv("example.csv", sep=';', header=None, names=['host', 'speed', 'time'])
print data[data.speed <= 950]

Get data from SQL¶

from pandas.io import sql
import sqlite3

conn = sqlite3.connect('example.sqlite3')
query = "SELECT * FROM mytable WHERE mycol = 'MUH';"

data = sql.read_frame(query, con=conn)
print data.describe()

Read data from the web¶

from urllib2 import urlopen
from StringIO import StringIO

url = urlopen('http://www.codekid.net/data_file.txt').read()
data = pd.read_table(StringIO(url), sep='\t')

Converter¶

replace all $ in salary column with nothing

data = pd.read_csv('example.csv',
                   converters={'salary': lambda x: float(x.replace('$', ''))})

Selecting stuff¶

where

print data[(data.speed <= 950) | (data.time > 0.2)]
print data[(data.speed > 950) & (data.time < 0.2)]

join

pd.merge(left_frame, right_frame, on='key', how='inner')
pd.merge(left_frame, right_frame, on='key', how='left')
pd.merge(left_frame, right_frame, on='key', how='right')

Merging¶

data_full = pd.concat([data, data_set2])
data_full = pd.merge(data, data_set2)

Aggregation¶

Group data by column title
Run np.size and np.mean functions on column rating

data.groupby('title').agg({'rating': [np.size, np.mean]})

Iteration¶

for entry in data[['date', 'value']].itertuples():
  print entry

Graphing¶

import pandas as pd
import matplotlib.pyplot as plt

data = pd.read_excel('example.xlsx', 'sheet1')
data.set_index("name", inplace=True)
plt.figure()
plt.title("Just a simple test")
data.plot(kind="barh")
plt.show()