导入和构造
# coding: utf-8
import numpy as np
import pandas as pd
from blaze import data, by
df = pd.DataFrame({'name': ['Alice', 'Bob', 'Joe', 'Bob'],
'amount': [100, 200, 300, 400],
'id': [1, 2, 3, 4],
})
df = data(df)
print(df.peek())输出：
name amount id
0 Alice 100 1
1 Bob 200 2
2 Joe 300 3
3 Bob 400 4| Computaion | Pandas | Blaze |
|---|---|---|
| Column Arithmetic | df.amount * 2 | df.amount * 2 |
| Multiple Columns | df[[‘id’, ‘amount’]] | df[[‘id’, ‘amount’]] |
| Selection | df[df.amount > 300] | df[df.amount > 300] |
| Group By | df.groupby(‘name’).amount.mean() df.groupby([‘name’,’id’]).amount.mean() | by(df.name, amount=df.amount.mean()) by(merge(df.name, df.id), amount=df.amount.mean()) |
| Join | pd.merge(df, df2, on=’name’) | join(df, df2, ‘name’) |
| Map | df.amount.map(lambda x: x + 1) | df.amount.map(lambda x: x + 1, ‘int64’) |
| Relabel Columns | df.rename(columns={‘name’: ‘alias’, ‘amount’: ‘dollars’}) | df.relabel(name=’alias’, amount=’dollars’) |
| Drop duplicates | df.drop_duplicates() df.name.drop_duplicates() | df.distinct() df.name.distinct() |
| Reductions | df.amount.mean() df.amount.value_counts() | df.amount.mean() df.amount.count_values() |
| Concatenate | pd.concat((df, df)) | concat(df, df) |
| Column Type Information | df.dtypes df.amount.dtype | df.dshape df.amount.dshape |
Blaze可以简化一些常见的IO任务，并使其更具可读性，这些任务是希望使用pandas处理的。这些例子使用的是odo库。许多情况下，blaze可以处理超出内存大小的数据集，而这是pandas不能够容易处理的事情。
from odo import odo| Operations | Pandas | Blaze |
|---|---|---|
| Load directory of CSV files | df = pd.concat([pd.read_csv(filename) for filename in glob.glob(‘path/to/*.csv’)]) | df = data(‘path/to/*.csv’) |
| Save result to CSV file | df[df.amount < 0].to_csv(‘output.csv’) | odo(df[df.amount < 0], ‘output.csv’) |
| Read from SQL database | df = pd.read_sql(‘select * from t’, con=’sqlite:///db.db’) df = pd.read_sql(‘select * from t’, con=sa.create_engine(‘sqlite:///db.db’)) | df = data(‘sqlite://db.db::t’) |
