Reindexing and Data Alignment

Two related ideas

• Reindexing — you explicitly ask pandas to rearrange an object onto a new index, introducing NaN for any label that wasn't already there.
• Alignment — pandas does this automatically whenever you do arithmetic between two Series or DataFrames with different indexes.

Both boil down to the same rule: match by label, not by position.

reindex: rearranging onto a new index

import pandas as pd
 
obj = pd.Series([4.5, 7.2, -5.3, 3.6], index=["d", "b", "a", "c"])
print(obj.reindex(["a", "b", "c", "d", "e"]))  # "e" wasn't there -> NaN

For ordered data, method="ffill" fills in the gaps using the last known value instead of leaving NaN:

import numpy as np
 
obj3 = pd.Series(["blue", "purple", "yellow"], index=[0, 2, 4])
print(obj3.reindex(np.arange(6), method="ffill"))

Reindexing a DataFrame — rows, columns, or both

frame = pd.DataFrame(
    np.arange(9).reshape((3, 3)),
    index=["a", "c", "d"],
    columns=["Ohio", "Texas", "California"],
)
 
print(frame.reindex(index=["a", "b", "c", "d"]))          # "b" is new -> NaN row
print(frame.reindex(columns=["Texas", "Utah", "California"]))  # "Utah" is new, "Ohio" dropped

Automatic alignment in arithmetic

You never call reindex yourself before adding two Series — pandas does it for you, using the union of both indexes:

s1 = pd.Series([7.3, -2.5, 3.4, 1.5], index=["a", "c", "d", "e"])
s2 = pd.Series([-2.1, 3.6, -1.5, 4, 3.1], index=["a", "c", "e", "f", "g"])
 
print(s1 + s2)

Labels present in only one of the two Series become NaN in the result — "d" and "f"/"g" never had a matching partner to add against.

Filling gaps during arithmetic: fill_value

If you'd rather treat a missing label as 0 (or some other value) instead of NaN, use the method form of the operator with fill_value:

df1 = pd.DataFrame(np.arange(9.0).reshape(3, 3), columns=list("bcd"))
df2 = pd.DataFrame(np.arange(12.0).reshape(4, 3), columns=list("bde"))
 
print(df1.add(df2, fill_value=0))

The same fill_value idea also works directly on reindex:

print(df1.reindex(columns=df2.columns, fill_value=0))

Common pitfalls

• df1 + df2 (the operator) always produces NaN for non-overlapping labels — use .add(), .sub(), .mul(), .div() with fill_value if you want zeros instead.
• reindex creates missing rows/columns; it never drops data implicitly unless a label from the original index is missing from the new one.
• method="ffill" on reindex requires the index to already be sorted in the order you expect the fill to follow.

Visualize it

  flowchart LR
  A["Series 1
index: a, c, d, e"] --> C["Union index
a, c, d, e, f, g"]
  B["Series 2
index: a, c, e, f, g"] --> C
  C --> D["s1 + s2
NaN where either side is missing"]

🧪 Try It Yourself

Exercise 1 – Reindex Onto a Larger Index

Press Run to see what your code prints.

Exercise 2 – Automatic Alignment in Arithmetic

Press Run to see what your code prints.

Exercise 3 – Fill Gaps Instead of Producing NaN

Press Run to see what your code prints.

Check your understanding

Next

With your index aligned the way you want, you're ready to narrow rows down further — continue to Filtering with Conditions (and, or, isin, query).