轉換大熊貓的數據類型列

Question

如果在一個數據幀的熊貓列timestampMs值是unicode型的，我們希望將其轉換爲一個float，有以下2種方法之間有什麼區別？

df['timestampMs'].map(lambda x: float(x)/1000) 

和

df['timestampMs'].astype('float')/1000 

由於它們似乎都得到相同的結果，這是優選的方法？

Answer 1

嗯......如果你關心速度，lambda方法對於小數據集來說要快一點點。對於大型數據集去爲.astype()方法（我還親自找到它更易讀）：

import time 
import timeit 
import pandas as pd 

num_elements = 100 
times = [unicode(time.clock()) for x in range(num_elements)] 

df = pd.DataFrame(times) 

def first_method(): 
    df[0].map(lambda x: float(x)/1000) 

def second_method(): 
    df[0].astype('float')/1000 

num_reps = 15000 

print("First method time for {} reps: {}".format(num_reps, timeit.timeit(first_method, number=num_reps))) 
print("Second method time for {} reps: {}".format(num_reps, timeit.timeit(second_method, number=num_reps))) 

當num_elements = 100我得到：

First method time for 15000 reps: 1.95685731342 
Second method time for 15000 reps: 2.22381265566 

當num_elements = 1000我得到：

First method time for 15000 reps: 12.0774245498 
Second method time for 15000 reps: 6.77670391568