pandas-appender 0.9.8

A helper class that makes appending to a Pandas DataFrame efficient

pandas-appender

Have you ever wanted to append a bunch of rows to a Pandas DataFrame? Turns out that it's extremely inefficient to do! For a large dataframe, you're supposed to make multiple dataframes and pd.concat() them instead.

Also, Pandas deprecated dataframe.append() in version 1.4 and intends to remove it in 2.0.

So... helper function? Pandas doesn't have one. Roll your own? Ugh. OK then: here's that helper function. It can append around 1 million very small rows per cpu-second. It has a modest additional memory usage of around 5 megabytes, dynamically growing with the number of rows appended.

Install

pip install pandas-appender

Usage

from pandas_appender import DF_Appender

dfa = DF_Appender(ignore_index=True)  # note that ignore_index moves to the init
for i in range(1_000_000):
    dfa = dfa.append({'i': i})

df = dfa.finalize()  # must call .finalize() before you can use the results

Type hints and category detection

Using narrower types and categories can often dramatically reduce the size of a DataFrame. There are two ways to do this in pandas-appender. One is to append to an existing dataframe:

dfa = DF_Appender(df, ignore_index=True)

and the second is to pass in a dtypes= argument:

dfa = DF_Appender(ignore_index=True, dtypes=another_dataframe.dtypes)

pandas-appender also offers a way to infer which columns would be smaller if they were categories. This code will either analyze an existing dataframe that you're appending to:

dfa = DF_Appender(df, ignore_index=True, infer_categories=True)

or it will analyze the first chunk of appended lines:

dfa = DF_Appender(ignore_index=True, infer_categories=True)

These inferred categories will override existing types or a dtypes= argument.

Incompatibilities with pandas.DataFrame.append()

DF_Appender must be finalized before use

• Pandas: df_new = df.append() # df_new is a dataframe
• DF_Appender: dfa_new = dfa.append() # must do df = dfa.finalize() to get a DataFrame

pandas.DataFame.append is idempotent, DF_Appender is not

• Pandas: df_new = df.append() # df is not changed
• DF_Appender: dfa_new = dfa.append() # modifies dfa, and dfa_new == dfa

pandas.DataFrame.append will promote types, while DF_Appender is strict

• Pandas: append 0.1 to an integer column, and the column will be promoted to float
• DF_Appender: when initialized with dtypes= or an existing DataFrame, appending 0.1 to an integer column causes 0.1 to be cast to an integer, i.e. 0.