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Use s.to_frame() to turn a pandas Series into a one-column DataFrame while keeping its index as the row labels. Use s.reset_index() when you want the former index labels included as ordinary columns. For a MultiIndex, choose reset_index() to expose levels as columns or unstack() to pivot a level across columns.
Convert a Series to one DataFrame column with to_frame()
Call to_frame() on the Series:
import pandas as pd
s = pd.Series([10, 20, 30], index=["a", "b", "c"], name="score")
df = s.to_frame()
The result is a DataFrame with one data column, and the Series index remains the DataFrame index. If the Series has a name, pandas uses it as the column label; if it does not, the column is unnamed. See the pandas Series.to_frame API.
Set or override the output column label with the name argument:
df = s.to_frame(name="values")Use this when the Series is unnamed or when you want the output schema to have a specific, readable column name.
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Put the Series index into DataFrame columns with reset_index()
When index labels are data you need to keep as columns, reset the index:
df = s.reset_index()By default, drop=False: pandas places the former index level or levels in column(s), followed by a column containing the Series values. A named index provides a useful label for its column; an unnamed index receives a default label. The pandas Series.reset_index API documents the parameters and return behavior.
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To name the values column, pass name:
df = s.reset_index(name="values")Here, name="values" labels the column containing the Series values; it does not set the label for the former index column.
Do not use drop=True when you need a DataFrame
s.reset_index(drop=True) discards the old index instead of adding it to the result. For a Series, that call returns a Series, not a DataFrame. Use the default drop=False when you want the index included as data columns.
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Choose the method that matches the output layout
| What you need | Use | Result |
|---|---|---|
| One data column, with current row labels kept as the index | s.to_frame() |
A one-column DataFrame; the column label uses the Series name when available. |
| One data column with a specified label | s.to_frame(name="values") |
A one-column DataFrame with the requested column name. |
| Former index labels included as data columns | s.reset_index() |
Index level column(s), followed by the Series values column. |
| Index labels included and values column named | s.reset_index(name="values") |
Former index column(s) plus a values column named values. |
| One MultiIndex level spread across columns | s.unstack() |
A pivoted DataFrame; the selected level becomes a column axis. |
Handle a MultiIndex Series
A Series can have more than one index level. Use s.reset_index() to make all levels columns, or pass level= to reset only selected levels and retain the remaining index structure. Use this when the intended layout is one row per Series entry with its index levels represented as fields.
Use s.unstack() for a different shape: it turns a MultiIndex Series into a DataFrame by pivoting an index level into columns. Choose it when the values should be arranged across columns rather than when you simply want each index level represented as a column. Check the resulting row and column layout for the level you intend to pivot; the pandas Series API reference lists unstack() alongside the other Series methods.
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