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Reindexing and Data Alignment

  • Reindexing — you explicitly ask pandas to rearrange an object onto a new index, introducing NaNNaN 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

reindex on a Series
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
reindex on a Series
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"method="ffill" fills in the gaps using the last known value instead of leaving NaNNaN:

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

reindex rows and columns
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
reindex rows and columns
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 reindexreindex yourself before adding two Series — pandas does it for you, using the union of both indexes:

Arithmetic aligns by label automatically
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)
Arithmetic aligns by label automatically
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 NaNNaN in the result — "d""d" and "f""f"/"g""g" never had a matching partner to add against.

Filling gaps during arithmetic: fill_value

If you’d rather treat a missing label as 00 (or some other value) instead of NaNNaN, use the method form of the operator with fill_valuefill_value:

add() 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))
add() 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_valuefill_value idea also works directly on reindexreindex:

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

Common pitfalls

  • df1 + df2df1 + df2 (the operator) always produces NaNNaN for non-overlapping labels — use .add().add(), .sub().sub(), .mul().mul(), .div().div() with fill_valuefill_value if you want zeros instead.
  • reindexreindex creates missing rows/columns; it never drops data implicitly unless a label from the original index is missing from the new one.
  • method="ffill"method="ffill" on reindexreindex requires the index to already be sorted in the order you expect the fill to follow.

Visualize it

diagram Two indexes aligning mermaid
Before an arithmetic operation, pandas takes the union of both indexes and reindexes each object onto it, introducing NaN for any label missing from one side.
sketch Aligning two mismatched indexes p5.js
Series 1 (amber) and Series 2 (blue) share some labels but not others; only the shared labels (green) produce a real result.

🧪 Try It Yourself

Exercise 1 – Reindex Onto a Larger Index

Exercise 2 – Automatic Alignment in Arithmetic

Exercise 3 – Fill Gaps Instead of Producing NaN

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).

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