Code can be lovely
Rounding numbers is probably one of the topics in primary school. In school, we’ve learned that half rounds up, anything less than half rounds down. For example, 0.5 rounds to 1, but 0.4 rounds to 1. Duh, I’m stating the obvious you think. It only came to my attention that this is NOT really the default behaviour in a very popular programming language: Python.
Here, is a simple example to try in your own Python console.
Continue reading “Number Rounding Business”Not an avid Pandas or Numpy user myself, but I had to spend some time lately to optimise probably a fairly common process: looking up a nested dictionary (2 or more levels) to find the right values element-wise for a column in a Pandas DataFrame. If it’s not clear, the problem I’m trying to solve here is to optimise a look-up function that can be done with .apply() to something more performant.
You might say, why not using .map()? Because the look-up function is not y = f(x), no, it is more like y = f(x, a) or even y = f(x, a, b), depending on the level of nestedness.
As mentioned earlier, this can be implemented with .apply() by supplying a Python function that does the look-up. However, .apply() is very slow (it’s not vectorised). The solution here is actually straightforward (I’m very new to Pandas and it took me some time to get here so I decided to make a note here for this). We first flatten the nested dictionary to have different levels of keys as tuples, which allows us to create a DataFrame with MultiIndex. With MultiIndex, we can easily apply .merge to join the DataFrame objects.
Hopefully the code snippet is more understandable.
import pandas as pd
nested_dict = {
"A": {
"Apple": "Red",
"Banana": "Green",
},
"B": {"Apple": "Green", "Banana": "Yellow"},
}
df = pd.DataFrame.from_dict(
{
"Fruit": {0: "Apple", 1: "Banana", 2: "Banana"},
"Price": {0: 0.911, 1: 1.734, 2: 1.844},
"Bucket": {0: "A", 1: "B", 2: "A"},
}
)
# Method 1: .apply()
# Apply Python function element-wise, as slow as a regular for loop
df1 = df.copy()
df1["Color"] = df1.apply(
lambda row: nested_dict.get(row["Bucket"], {}).get(row["Fruit"]), axis=1
)
print(df1)
# Method 2: .merge()
# Vectorized, much faster (even though the big O is the same)
df2 = df.copy()
# The only overhead is to construct a dataframe with 'MultiIndex'
nested_df = pd.DataFrame.from_dict(
{
(inner_key, outer_key): value
for outer_key, outer_value in nested_dict.items()
for inner_key, value in outer_value.items()
},
orient="index",
)
nested_df.index = pd.MultiIndex.from_tuples(nested_df.index)
nested_df.rename(columns={0: "Color"}, inplace=True)
df2 = df2.merge(nested_df, how="left", left_on=("Fruit", "Bucket"), right_index=True)
print(df2)
去年就把手上的Chromebook在eBay賣掉，加了點錢在Amazon買了一台華碩的7英吋平板電腦。上禮拜買了羅技的藍牙鍵盤，這下和平板電腦配合在一起就真的能當台小電腦💻用了。所以有琢磨著怎樣可以在不方便攜帶筆電的情況下用來旅途中練習一些算法題。
熟悉程式設計的（我也希望都是在UNIX環境下的）或多或少可能有過想在平板電腦上偶爾練練手的想法，Android畢竟是基於Linux設計的，加上系統多年的更新換代發展，灌上一個Linux的工作環境應該不會太難，而且將會是一個非常大的效率提升。不過如果真的灌一個完整的Linux環境，一來是佔用很大的磁盤空間，二來電力損耗估計也是會讓人頭疼。好在我在Google馬上找到了Termux，安裝步驟簡單到不行，直接在Google Play點選安裝即可。
本文的目的绝非为了盗版之用，而是很多美国亚马逊买的Kindle设备（如Kindle 4、Kindle 5）无法在中国亚马逊里注册，不能通过正常的推送渠道购买正版电子书，因此这是一个万不得已的做法，在中国亚马逊买书然后破解再传到美版（或日版）Kindle里。
需要下载的软件有：Calibre、DeDRM插件、Kindle for PC（或者for Mac）
在Terminal里测试python3命令吧，如果出现下图所示界面就成功了。图已破，推他已死。