Python – Improve Row Append Performance On Pandas DataFrames

numpypandaspythonpython-2.7

I am running a basic script that loops over a nested dictionary, grabs data from each record, and appends it to a Pandas DataFrame. The data looks something like this:

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

In total it has a few million records. The script itself looks like this:

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)

This runs painfully slow, however. Before I look for a way to parallelize it, I just want to make sure I'm not missing something obvious that would make this perform faster as it is, as I'm still quite new to Pandas.

Best Answer

I also used the dataframe's append function inside a loop and I was perplexed how slow it ran.

A useful example for those who are suffering, based on the correct answer on this page.

Python version: 3

Pandas version: 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")

The "from_dict" function: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.from_dict.html

Related Topic