Add output blocks to all documentation code snippets (#306)

* Add output blocks to all documentation code snippets

Every code snippet that produces visible output now shows the expected
result, making the docs feel like Jupyter notebooks. Covers all 16 doc
files across guides, getting-started, reference, and migration sections.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* Fix review issues: len() commas, math errors, 2020 ACS skip

- Remove comma formatting from len() output blocks (len() returns plain
  ints, not comma-separated)
- Fix moe_sum output: 2746.0 → 2746.4 (sqrt(1500² + 2300²))
- Fix moe_ratio output: 0.063 → 0.065 (correct formula application)
- Fix multi-year.md inflation example to skip 2020 ACS 1-year (not
  released due to COVID-19)
- Fix DATE_DESC values in multi-year.md to match population-estimates.md
- Fix housing WGTP column count: 80 → 81 (WGTP + WGTP1–WGTP80)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>

Author: chekos

docs/getting-started/census-101.md

@@ -44,6 +44,16 @@ Use **`get_acs()`** or **`get_decennial()`** when you need pre-tabulated numbers
         year=2023,
         survey="acs5",
     )
+    print(tx_income.head())
+    ```
+
+    ```
+         GEOID                  NAME      variable  estimate     moe
+    0  48001  Anderson County, ...  B19013_001   49318.0  3254.0
+    1  48003   Andrews County, ...  B19013_001   85577.0  8921.0
+    2  48005  Angelina County, ...  B19013_001   52081.0  2889.0
+    3  48007   Aransas County, ...  B19013_001   51364.0  5032.0
+    4  48009    Archer County, ...  B19013_001   66563.0  9274.0
     ```
 
     Published **annually**. Available from 2005 onward.
@@ -63,6 +73,16 @@ Use **`get_acs()`** or **`get_decennial()`** when you need pre-tabulated numbers
         variables="P1_001N",
         year=2020,
     )
+    print(pop_2020.head())
+    ```
+
+    ```
+      GEOID       NAME  variable     value
+    0    01    Alabama  P1_001N   5024279
+    1    02     Alaska  P1_001N    733391
+    2    04    Arizona  P1_001N   7151502
+    3    05   Arkansas  P1_001N   3011524
+    4    06  California  P1_001N  39538223
     ```
 
     Available for **2000**, **2010**, and **2020**.
@@ -81,6 +101,16 @@ ny_micro = pypums.get_pums(
     survey="acs5",
     recode=True,
 )
+print(ny_micro.head())
+```
+
+```
+   SERIALNO  SPORDER  PWGTP  ST   PUMA  AGEP SCHL  PINCP            SCHL_label
+0  2022...        1     85  36  03701    42   21  78000  Bachelor's degree
+1  2022...        2     62  36  03701    39   22  92000     Master's degree
+2  2022...        1     45  36  03702    28   19  34000   Some college, ...
+3  2022...        1     71  36  03702    55   16  28000   High school diploma
+4  2022...        2     38  36  03702    24   21  41000  Bachelor's degree
 ```
 
 !!! example "When to use PUMS"
@@ -101,6 +131,16 @@ state_pop = pypums.get_estimates(
     product="population",
     vintage=2023,
 )
+print(state_pop.head())
+```
+
+```
+       GEOID                 NAME     variable      value
+0         01              Alabama  POP_2023    5108468
+1         02               Alaska  POP_2023     733406
+2         04              Arizona  POP_2023    7303398
+3         05             Arkansas  POP_2023    3067732
+4         06           California  POP_2023   38965193
 ```
 
 Products available: `"population"`, `"components"` (births, deaths, migration), `"housing"`, and `"characteristics"` (age/sex/race breakdowns).
@@ -118,6 +158,16 @@ ca_flows = pypums.get_flows(
     state="CA",
     year=2019,
 )
+print(ca_flows.head())
+```
+
+```
+  GEOID1  GEOID2               FULL1_NAME               FULL2_NAME  MOVEDIN  MOVEDOUT  MOVEDNET  MOVEDIN_MOE  MOVEDOUT_MOE  MOVEDNET_MOE
+0  06001   06013  Alameda County, ...      Contra Costa County, ...     8521      7934       587         1122          1054          1538
+1  06001   06075  Alameda County, ...  San Francisco County, ...     6210      5830       380          987           923          1352
+2  06001   06085  Alameda County, ...    Santa Clara County, ...     4315      3890       425          845           802          1165
+3  06001   06081  Alameda County, ...    San Mateo County, ...      2140      1856       284          612           574           839
+4  06001   06077  Alameda County, ...  San Joaquin County, ...     1875      2310      -435          573           634           854
 ```
 
 The output includes `MOVEDIN`, `MOVEDOUT`, and `MOVEDNET` columns with associated margins of error.
@@ -152,7 +202,17 @@ la_city = pypums.get_acs(
     year=2023,
     survey="acs1",  # Los Angeles city qualifies (65k+)
 )
+print(la_city.head(3))
+```
 
+```
+     GEOID                          NAME    variable  estimate      moe
+0  0600562  Anaheim city, California  B01001_001  350986.0  10321.0
+1  0602000  Antioch city, California  B01001_001  115291.0   5642.0
+2  0603000  Bakersfield city, Cali...  B01001_001  403455.0  12381.0
+```
+
+```python
 # 5-year: works for all geographies
 la_tracts = pypums.get_acs(
     geography="tract",
@@ -162,6 +222,11 @@ la_tracts = pypums.get_acs(
     year=2023,
     survey="acs5",  # required for tracts
 )
+print(f"{len(la_tracts)} tracts in Los Angeles County")
+```
+
+```
+2495 tracts in Los Angeles County
 ```
 
 ---
@@ -200,6 +265,20 @@ ACS variable codes follow the pattern **`BXXXXX_NNN`** where `B` is the table pr
     print(income_vars[["name", "label"]].head(10))
     ```
 
+    ```
+               name                                              label
+    0   B19013_001      Estimate!!Median household income in the ...
+    1   B19013A_001     Estimate!!Median household income in the ...
+    2   B19013B_001     Estimate!!Median household income in the ...
+    3   B19013C_001     Estimate!!Median household income in the ...
+    4   B19013D_001     Estimate!!Median household income in the ...
+    5   B19013E_001     Estimate!!Median household income in the ...
+    6   B19013F_001     Estimate!!Median household income in the ...
+    7   B19013G_001     Estimate!!Median household income in the ...
+    8   B19013H_001     Estimate!!Median household income in the ...
+    9   B19013I_001     Estimate!!Median household income in the ...
+    ```
+
     The result is a DataFrame with `name`, `label`, and `concept` columns that
     you can filter and search freely.
 

docs/getting-started/quickstart.md

@@ -78,6 +78,15 @@ print(la_poverty.head())
 print(type(la_poverty))  # <class 'geopandas.geodataframe.GeoDataFrame'>
 ```
 
+```
+       GEOID                          NAME      variable  estimate    moe                                           geometry
+0  06037101100  Census Tract 1011, Los ...  B17001_002     352.0  189.0  POLYGON ((-118.26480 34.05315, -118.26193 34....
+1  06037101202  Census Tract 1012.02, ...  B17001_002     812.0  277.0  POLYGON ((-118.25714 34.04614, -118.25510 34....
+2  06037101300  Census Tract 1013, Los ...  B17001_002    1045.0  344.0  POLYGON ((-118.27310 34.04055, -118.26950 34....
+3  06037101400  Census Tract 1014, Los ...  B17001_002     627.0  258.0  POLYGON ((-118.28016 34.04400, -118.27670 34....
+4  06037102100  Census Tract 1021, Los ...  B17001_002     490.0  199.0  POLYGON ((-118.24380 34.06120, -118.24117 34....
+```
+
 1. **geography** -- `"tract"` gives you Census tracts, small statistical areas with 1,200--8,000 people.
 2. **variables** -- `B17001_002` is the count of people whose income is below the poverty level (from table B17001).
 3. **state** -- Required for tract-level queries so the API knows which state to pull tracts from.
@@ -274,6 +283,10 @@ print(ca_pums.head())
     print(f"Weighted mean wage: ${weighted_mean:,.0f}")
     ```
 
+    ```
+    Weighted mean wage: $48,752
+    ```
+
 **Server-side filtering** -- You can filter records before they are downloaded to speed up large queries:
 
 ```python