Why am I getting "AttributeError: 'DataFrame' object has no attribute 'append'?

pandas >= 2.0 append has been removed, use pd.concat instead¹

Starting from pandas 2.0, append has been removed from the API. It was previously deprecated in version 1.4. See the docs on Deprecations as well as this github issue that originally proposed its deprecation.

The rationale for its removal was to discourage iteratively growing DataFrames in a loop (which is what people typically use append for). This is because append makes a new copy at each stage, resulting in quadratic complexity in memory.

1. This assume you're appending one DataFrame to another. If you're appending a row to a DataFrame, the solution is slightly different - see below.

The idiomatic way to append DataFrames is to collect all your smaller DataFrames into a list, and then make one single call to pd.concat. Here's a(n oversimplified) example

df_list = []
for df in some_function_that_yields_dfs():
    df_list.append(df)

final_df = pd.concat(df_list)

Note that if you are trying to append one row at a time rather than one DataFrame at a time, the solution is even simpler.

data = []
for a, b, c from some_function_that_yields_data():
    data.append([a, b, c])

df = pd.DataFrame(data, columns=['a', 'b', 'c'])

More information in Creating an empty Pandas DataFrame, and then filling it?

The accepted answer will break if there are duplicate headers:

InvalidIndexError: Reindexing only valid with uniquely valued Index objects.

For example, here A has 3x trial columns, which prevents concat:

A = pd.DataFrame([[3, 1, 4, 1]], columns=['id', 'trial', 'trial', 'trial'])
#    id  trial  trial  trial
# 0   3      1      4      1

B = pd.DataFrame([[5, 9], [2, 6]], columns=['id', 'trial'])
#    id  trial
# 0   5      9
# 1   2      6

A_B = pd.concat([A, B], ignore_index=True)
# InvalidIndexError: Reindexing only valid with uniquely valued Index objects

To fix this, deduplicate the column names before you concat:

• pandas 2.0+

  for df in [A, B]:
      df.columns = pd.io.common.dedup_names(df.columns, is_potential_multiindex=False)
  
  A_B = pd.concat([A, B], ignore_index=True)
  #    id  trial  trial.1  trial.2
  # 0   3      1        4        1
  # 1   5      9      NaN      NaN
  # 2   2      6      NaN      NaN
  

• pandas < 2.0

  parser = pd.io.parsers.base_parser.ParserBase({'usecols': None})
  
  for df in [A, B]:
      df.columns = parser._maybe_dedup_names(df.columns) 
  
  A_B = pd.concat([A, B], ignore_index=True)
  #    id  trial  trial.1  trial.2
  # 0   3      1        4        1
  # 1   5      9      NaN      NaN
  # 2   2      6      NaN      NaN
  

• pandas < 1.3

  parser = pd.io.parsers.ParserBase({})
  
  for df in [A, B]:
      df.columns = parser._maybe_dedup_names(df.columns) 
  
  A_B = pd.concat([A, B], ignore_index=True)
  #    id  trial  trial.1  trial.2
  # 0   3      1        4        1
  # 1   5      9      NaN      NaN
  # 2   2      6      NaN      NaN
  

You can also use:

dat1 = pd.concat([dat1, dat2], axis=1)