append equivalent with concat in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the append() method do in pandas?

It adds rows of one DataFrame to the end of another DataFrame, combining them vertically.

beginner

How can you combine two DataFrames vertically without using append()?

You can use pd.concat([df1, df2]) to join DataFrames vertically, which is the recommended way since append() is deprecated.

intermediate

What is the main difference between append() and concat() in pandas?

append() is a simpler method to add rows but is deprecated. concat() is more flexible and can combine multiple DataFrames along different axes.

beginner

Show a simple example of using pd.concat() to combine two DataFrames df1 and df2.

Example:<br><pre>import pandas as pd

df1 = pd.DataFrame({'A': [1, 2]})
df2 = pd.DataFrame({'A': [3, 4]})

result = pd.concat([df1, df2])
print(result)</pre>

beginner

Why is pd.concat() preferred over append() in recent pandas versions?

append() is deprecated and will be removed in future versions. pd.concat() is more powerful and consistent for combining DataFrames.

Which pandas function is recommended to combine DataFrames vertically now that append() is deprecated?

A pd.merge()

B pd.groupby()

C pd.join()

D pd.concat()

pd.concat() is the recommended function to combine DataFrames vertically, replacing append().

What does pd.concat([df1, df2]) do by default?

A Joins DataFrames vertically (rows)

B Joins DataFrames horizontally (columns)

C Merges DataFrames on a key

D Creates a new DataFrame with duplicated rows

By default, pd.concat() stacks DataFrames vertically (adds rows).

If you want to combine DataFrames side-by-side (columns), which pd.concat() parameter should you change?

A axis=1

B axis=0

C join='inner'

D ignore_index=True

Setting axis=1 concatenates DataFrames horizontally (by columns).

What happens if you use pd.concat() on DataFrames with different columns?

A It raises an error

B It fills missing columns with NaN

C It drops columns not common to all

D It merges only common columns

Missing columns in each DataFrame are filled with NaN when concatenated.

Why might you want to use ignore_index=True with pd.concat()?

A To merge on index

B To keep original indexes

C To reset the row index in the combined DataFrame

D To sort the DataFrame

ignore_index=True resets the row index after concatenation.

Explain how to combine two pandas DataFrames vertically without using the deprecated append() method.

Think about a function that stacks DataFrames on top of each other.

Describe the differences and advantages of using pd.concat() over append() in pandas.

Consider flexibility and future compatibility.