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.

Practice

1. What is the main reason to use pandas.concat() instead of DataFrame.append()?

easy

A. concat() is faster for single DataFrame addition only

B. Because append() is deprecated and concat() can join multiple DataFrames at once

C. append() works only for columns, not rows

D. concat() automatically sorts columns alphabetically

Solution

1. Step 1: Understand the deprecation of append()

   The append() method is deprecated in pandas and will be removed in future versions.

2. Step 2: Recognize concat() advantages

   concat() can join multiple DataFrames at once, not just one, making it more flexible.

3. Final Answer:

   Because append() is deprecated and concat() can join multiple DataFrames at once -> Option B

4. Quick Check:

   append() deprecated, concat() multi-join [OK]

Hint: Remember: concat replaces append and handles many DataFrames [OK]

Common Mistakes:

• Thinking append is still recommended
• Believing concat only works for columns
• Assuming append is faster than concat

2. Which of the following is the correct syntax to replace df1.append(df2, ignore_index=True) using concat?

easy

A. pd.concat([df1, df2], ignore_index=True)

B. pd.concat([df1, df2], axis=0)

C. pd.concat(df1, df2, ignore_index=True)

D. pd.concat([df1, df2], axis=1, ignore_index=True)

Solution

1. Step 1: Identify the correct function call

   pd.concat() requires a list of DataFrames as the first argument, so [df1, df2] is correct.

2. Step 2: Use ignore_index=True to reset row numbers

   To mimic append(ignore_index=True), pass ignore_index=True to concat(). The default axis is 0 (rows), so no need to specify.

3. Final Answer:

   pd.concat([df1, df2], ignore_index=True) -> Option A

4. Quick Check:

   List of DataFrames + ignore_index=True = correct concat syntax [OK]

Hint: Use list of DataFrames and ignore_index=True with concat [OK]

Common Mistakes:

• Passing DataFrames without list brackets
• Using axis=1 to add rows (wrong axis)
• Omitting ignore_index when needed

3. Given the code:

import pandas as pd

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

What will be the output?

medium

A. A
0 1
1 2
0 3
1 4

B. A
0 3
1 4

C. A
0 1
1 2
2 3
3 4

D. Error because ignore_index is not valid

Solution

1. Step 1: Understand concat with ignore_index=True

   concat joins rows from both DataFrames and resets the index to 0,1,2,...

2. Step 2: Combine rows and reset index

   Rows from df1 (1,2) and df2 (3,4) stack vertically with new index 0 to 3.

3. Final Answer:

   A 0 1 1 2 2 3 3 4 -> Option C

4. Quick Check:

   concat stacks rows and resets index with ignore_index=True [OK]

Hint: concat stacks rows; ignore_index resets numbering [OK]

Common Mistakes:

• Expecting original indexes to remain
• Confusing axis parameter
• Thinking concat drops rows

4. What is wrong with this code snippet?

import pandas as pd

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

medium

A. The DataFrames should be passed as a list inside concat

B. ignore_index is not a valid argument for concat

C. concat cannot join DataFrames with same columns

D. The code will run without errors and print combined DataFrame

Solution

1. Step 1: Check concat argument format

   pd.concat() expects a list or iterable of DataFrames as the first argument, but here two DataFrames are passed as separate arguments.

2. Step 2: Identify correct usage

   Correct usage is pd.concat([df1, df2], ignore_index=True) with square brackets around DataFrames.

3. Final Answer:

   The DataFrames should be passed as a list inside concat -> Option A

4. Quick Check:

   concat needs list of DataFrames [OK]

Hint: Always pass DataFrames as a list to concat [OK]

Common Mistakes:

• Passing DataFrames as separate arguments
• Forgetting brackets around DataFrames
• Misusing ignore_index parameter

5. You have three DataFrames:

df1 = pd.DataFrame({'X': [1, 2]})
df2 = pd.DataFrame({'X': [3], 'Y': [5]})
df3 = pd.DataFrame({'X': [4], 'Y': [6]})

You want to combine them vertically using concat so that missing columns fill with NaN and the index resets. Which code achieves this?

hard

A. pd.concat(df1, df2, df3, ignore_index=True)

B. pd.concat([df1, df2, df3], axis=1, ignore_index=True)

C. pd.concat([df1, df2, df3], join='inner', ignore_index=True)

D. pd.concat([df1, df2, df3], ignore_index=True)

Solution

1. Step 1: Combine DataFrames vertically with concat

   Use pd.concat() with a list of DataFrames to stack rows.

2. Step 2: Handle missing columns and reset index

   Default join='outer' fills missing columns with NaN. Use ignore_index=True to reset row numbers.

3. Final Answer:

   pd.concat([df1, df2, df3], ignore_index=True) -> Option D

4. Quick Check:

   concat list, default outer join, ignore_index=True [OK]

Hint: Use concat list with ignore_index=True for clean vertical join [OK]

Common Mistakes:

• Using axis=1 to join rows (actually joins columns)
• Using join='inner' which drops columns
• Passing DataFrames without list brackets