Don’t append rows to a pandas DataFrame

Most pandas users encounter a situation where choosing to append rows to a pandas DataFrame seems like a good idea. A quick search of the API (or your favorite search engine) reveals that pandas has an append method in DataFrame. You may be tempted to use it. In this article I’ll show you why you should not use append, how you should grow your DataFrame, and a tip to make it faster.

Since pandas version 1.4.0, the append method has been deprecated, and this is called out in the documentation. The recommended solution is to use concat, but there’s a little more to the idea of growing a DataFrame than just blindly calling concat. Let’s dig into some examples to see why.

An example

Consider a scenario where we have a DataFrame with time series data. That just means that the index is a datetime, and in our case, it has one row per day.

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.rand(365,3), index=pd.date_range('1999-01-01', periods=365), columns=list('ABC'))

                   A         B         C
1999-12-27  0.731785  0.737431  0.422735
1999-12-28  0.378716  0.880707  0.775295
1999-12-29  0.002116  0.417487  0.427373
1999-12-30  0.012588  0.455592  0.022638
1999-12-31  0.026779  0.337595  0.894299

This is fabricated data, but you could imagine that you have a data file with data for each year, month, or week, for example. Now let’s say that the approach you choose is is to load the first data file into a DataFrame, then load each file (as a DataFrame) and append it your initial DataFrame. How would append work?

# data for year 2000
df2 = pd.DataFrame(np.random.rand(365,3), index=pd.date_range('2000-01-01', periods=365), columns=list('ABC'))
df_combined = df.append(df2)

(730, 3)
<ipython-input-2-58251cd78b0a>:3: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  df_combined = df.append(df2)

                   A         B         C
2000-12-26  0.900865  0.054083  0.103369
2000-12-27  0.433142  0.325692  0.412640
2000-12-28  0.877837  0.766650  0.244791
2000-12-29  0.095600  0.821537  0.685641
2000-12-30  0.095580  0.501606  0.856036

append is deprecated

The first thing we see is that since I’m running pandas >= 1.4.0, there is a deprecation warning. It’s telling us to switch from append to concat. So if all you want to know is how you should append rows to an existing DataFrame, use pd.concat.

How to use pd.concat

pd.concat takes a list of pandas objects to concatenate as the only required argument. If you call it this way, it will use the index on the objects (they could be DataFrames or Series) you pass in, and concatenate them along the 0 (or index) axis. If you pass in 1 (or columns) as the axis argument, it will grow the DataFrame by adding columns.

You also should know about the ignore_index parameter. This is False by default, but if the indexes overlap on your objects (for example, if you have a default index on your DataFrames and truly intend to just have those rows added at the end), then you should set that to True.

It also takes a join argument, which defaults to outer. This means that missing values in one DataFrame will be created in the resulting DataFrame but with NaN values.

There are other arguments which can be useful, check the docs on merging for some good examples.

pd.concat([df, df2])
                   A         B         C
1999-01-01  0.190596  0.369787  0.503640
1999-01-02  0.407761  0.114384  0.044529
1999-01-03  0.782395  0.578529  0.426242
1999-01-04  0.996079  0.796410  0.246697
1999-01-05  0.409903  0.112034  0.602407
...              ...       ...       ...
2000-12-26  0.900865  0.054083  0.103369
2000-12-27  0.433142  0.325692  0.412640
2000-12-28  0.877837  0.766650  0.244791
2000-12-29  0.095600  0.821537  0.685641
2000-12-30  0.095580  0.501606  0.856036

[730 rows x 3 columns]

Building up a DataFrame

OK, now that you know the right answer to combining multiple DataFrames into one DataFrame, you know enough to use the correct method. But what about the situation where you end up getting multiple rows or smaller DataFrames one at a time and want to build one larger DataFrame? How should you do that?

Indexing in pandas can be so confusing

There are so many ways to do the same thing! What is the difference between .loc, .iloc, .ix, and []?  You can read the official documentation but there's so much of it and it seems so confusing. You can ask a question on Stack Overflow, but you're just as likely to get too many different and confusing answers as no answer at all. And existing answers don't fit your scenario.

You just need to get started with the basics.

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Let’s start with a simple example of our time series data, pretending we get one file per year.

df_all = pd.DataFrame()
for y in range(1999, 2023):
    # pretend we're loading these from a file or API or database
    df_y = pd.DataFrame(np.random.rand(365,3), index=pd.date_range(f'{y}-01-01', periods=365), columns=list('ABC'))
    # the temptation is to do this:
    #  df_combined = df_combined.append(df_y)
    df_all = pd.concat([df_all, df_y])
11.9 ms ± 392 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

A better way

That works. But a better way to do it is to do the pd.concat only one time.

data = []
for y in range(1999, 2023):
    data.append(pd.DataFrame(np.random.rand(365,3), index=pd.date_range(f'{y}-01-01', periods=365), columns=list('ABC')))
df_all = pd.concat(data)
7.2 ms ± 221 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

You might think, “What’s the big deal, it’s only a few more milliseconds”? Well, this is a trivial example of 365 data points. We can build a more complete example with a bit more data. Let’s say we have DataFrames of size 1,000 x 1,000, and let’s build them by calling pd.concat in two situations: once for each loop and only at the end. Let’s also build an example where we start with a larger DataFrame, but only append one row.

(Note I set ignore_index=True here because each DataFrame will have the same default index.)

import datetime

start =
times = []
df_all = pd.DataFrame()
# appending each time
for y in range(100):
    df = pd.DataFrame(np.random.rand(1_000, 1_000))
    df_all = pd.concat([df_all, df], ignore_index=True)
    duration = - start
    times.append((duration, y))
print(f"{duration} to make final DataFrame of dimensions {df_all.shape} calling concat every iteration")

start =
times2 = []        
data = []      
# just making the DataFrames once in the loop
for y in range(100):
    df = pd.DataFrame(np.random.rand(1_000, 1_000))
    duration = - start 
    times2.append((duration, y))
# and then one big final concat
df_all2 = pd.concat(data, ignore_index=True)
duration = - start
times2.append((duration, y))
print(f"{duration} to make final DataFrame of dimensions {df_all2.shape} calling concat once")

start =
times3 = []
df_all3 = pd.DataFrame(np.random.rand(100_000, 1_000))
# appending each time, but just a single row
for y in range(100):
    one_row_df = pd.DataFrame(np.random.rand(1, 1_000))
    df_all3 = pd.concat([df_all3, one_row_df], ignore_index=True)
    duration = - start
    times3.append((duration, y))
print(f"{duration} to make final DataFrame of dimensions {df_all3.shape} calling concat every iteration")
0:00:27.403460 to make final DataFrame of dimensions (100000, 1000) calling concat every iteration
0:00:01.383713 to make final DataFrame of dimensions (100000, 1000) calling concat once
0:00:50.220051 to make final DataFrame of dimensions (100100, 1000) calling concat every iteration
import matplotlib.pyplot as plt

axis = plt.subplot()
plt.plot(pd.DataFrame(times).set_index(1), label='Concat Every iteration')
plt.plot(pd.DataFrame(times2).set_index(1), label='Concat once at end')
plt.plot(pd.DataFrame(times3).set_index(1), label='Start big, one row every iteration')
axis.set_title('Time to append DataFrames')
axis.set_xlabel('Number of iterations')
growth of various append methods to a pandas dataframe

Dramatic, isn’t it?

Now that’s pretty dramatic, and hopefully illustrates clearly the cost of naively appending to DataFrames. When you append rows to an existing DataFrame, all the rows in the current DataFrame are copied each time you call append. If you start with a large DataFrame but only append one row in a loop, the time to add new rows will grow with the number of rows in the total DataFrame. However, if you are growing a DataFrame from scratch, it might seem fast enough in the beginning, but will bog down at large sizes.

Let’s just take a step back and talk about what the problem is. The first and most important thing to realize is that appending to a DataFrame will make a copy of the data. This means that every time you call pd.concat (or the deprecated append) every row in the first DataFrame has to be copied once to the new result DataFrame. If you start with a DataFrame with N rows and append M rows or DataFrames one at a time, you have to make NxM copies of just the original data, and this will be even worse if you are appending large chunks of data.

If there is only one final concatenation, the NxM copies are reduced to just one copy of the N rows.

So when you need to add data to your DataFrame and are tempted to do this in a loop, remember the correct way to do it and only call pd.concat once.