python - 提高 Pandas DataFrames 的行追加性能

Question

我正在运行一个循环遍历嵌套字典的基本脚本，从每条记录中获取数据，并将其附加到 Pandas DataFrame。数据看起来像这样:

data = {"SomeCity": {"Date1": {record1, record2, record3, ...}, "Date2": {}, ...}, ...}

它总共有几百万条记录。脚本本身如下所示:

city = ["SomeCity"]
df = DataFrame({}, columns=['Date', 'HouseID', 'Price'])
for city in cities:
    for dateRun in data[city]:
        for record in data[city][dateRun]:
            recSeries = Series([record['Timestamp'], 
                                record['Id'], 
                                record['Price']],
                                index = ['Date', 'HouseID', 'Price'])
            FredDF = FredDF.append(recSeries, ignore_index=True)

然而，这运行起来非常慢。在我寻找一种并行化方法之前，我只是想确保我没有遗漏一些明显的东西，这些东西会使它按原样执行得更快，因为我对 Pandas 还是很陌生。

Answer 1

我还在一个循环中使用了数据帧的append函数，我很困惑它运行得有多慢。

基于本页上的正确答案，对于那些正在受苦的人来说是一个有用的例子。

Python 版本:3

Pandas 版本:0.20.3

# the dictionary to pass to pandas dataframe
d = {}

# a counter to use to add entries to "dict"
i = 0 

# Example data to loop and append to a dataframe
data = [{"foo": "foo_val_1", "bar": "bar_val_1"}, 
       {"foo": "foo_val_2", "bar": "bar_val_2"}]

# the loop
for entry in data:

    # add a dictionary entry to the final dictionary
    d[i] = {"col_1_title": entry['foo'], "col_2_title": entry['bar']}
    
    # increment the counter
    i = i + 1

# create the dataframe using 'from_dict'
# important to set the 'orient' parameter to "index" to make the keys as rows
df = DataFrame.from_dict(d, "index")

“from_dict”函数:https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.from_dict.html