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docs: fix typo for "population" in the GeminiTextGenerator.predict(..., output_schema={...}) sample notebook #1748

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docs: fix typo for "population" in the GeminiTextGenerator.predict(..., output_schema={...}) sample notebook #1748

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86c1c29
docs: fix type in output_schema sample notebook
tswast May 19, 2025
14efcd3
Update bq_dataframes_llm_output_schema.ipynb
tswast May 19, 2025
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26 changes: 13 additions & 13 deletions notebooks/generative_ai/bq_dataframes_llm_output_schema.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -361,7 +361,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also get float or int values, for example, to get polulations in millions:"
"You can also get float or int values, for example, to get populations in millions:"
]
},
{
Expand Down Expand Up @@ -400,7 +400,7 @@
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>city</th>\n",
" <th>polulation_million</th>\n",
" <th>population_in_millions</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
Expand All @@ -425,7 +425,7 @@
"</div>[3 rows x 2 columns in total]"
],
"text/plain": [
" city polulation_million\n",
" city population_in_millions\n",
"0 Seattle 0.75\n",
"1 New York 19.68\n",
"2 Shanghai 26.32\n",
Expand All @@ -439,8 +439,8 @@
}
],
"source": [
"result = gemini.predict(df, prompt=[\"what is the population in millions of\", df[\"city\"]], output_schema={\"polulation_million\": \"float64\"})\n",
"result[[\"city\", \"polulation_million\"]]"
"result = gemini.predict(df, prompt=[\"what is the population in millions of\", df[\"city\"]], output_schema={\"population_in_millions\": \"float64\"})\n",
"result[[\"city\", \"population_in_millions\"]]"
]
},
{
Expand Down Expand Up @@ -576,7 +576,7 @@
" <th></th>\n",
" <th>city</th>\n",
" <th>is_US_city</th>\n",
" <th>polulation_in_millions</th>\n",
" <th>population_in_millions</th>\n",
" <th>rainy_days_per_year</th>\n",
" </tr>\n",
" </thead>\n",
Expand Down Expand Up @@ -608,7 +608,7 @@
"</div>[3 rows x 4 columns in total]"
],
"text/plain": [
" city is_US_city polulation_in_millions rainy_days_per_year\n",
" city is_US_city population_in_millions rainy_days_per_year\n",
"0 Seattle True 0.75 152\n",
"1 New York True 8.8 121\n",
"2 Shanghai False 26.32 115\n",
Expand All @@ -622,8 +622,8 @@
}
],
"source": [
"result = gemini.predict(df, prompt=[df[\"city\"]], output_schema={\"is_US_city\": \"bool\", \"polulation_in_millions\": \"float64\", \"rainy_days_per_year\": \"int64\"})\n",
"result[[\"city\", \"is_US_city\", \"polulation_in_millions\", \"rainy_days_per_year\"]]"
"result = gemini.predict(df, prompt=[df[\"city\"]], output_schema={\"is_US_city\": \"bool\", \"population_in_millions\": \"float64\", \"rainy_days_per_year\": \"int64\"})\n",
"result[[\"city\", \"is_US_city\", \"population_in_millions\", \"rainy_days_per_year\"]]"
]
},
{
Expand Down Expand Up @@ -677,7 +677,7 @@
" <th></th>\n",
" <th>city</th>\n",
" <th>is_US_city</th>\n",
" <th>polulation_in_millions</th>\n",
" <th>population_in_millions</th>\n",
" <th>rainy_days_per_year</th>\n",
" <th>places_to_visit</th>\n",
" <th>gps_coordinates</th>\n",
Expand Down Expand Up @@ -717,7 +717,7 @@
"</div>[3 rows x 6 columns in total]"
],
"text/plain": [
" city is_US_city polulation_in_millions rainy_days_per_year \\\n",
" city is_US_city population_in_millions rainy_days_per_year \\\n",
"0 Seattle True 0.74 150 \n",
"1 New York True 8.4 121 \n",
"2 Shanghai False 26.32 115 \n",
Expand All @@ -741,8 +741,8 @@
}
],
"source": [
"result = gemini.predict(df, prompt=[df[\"city\"]], output_schema={\"is_US_city\": \"bool\", \"polulation_in_millions\": \"float64\", \"rainy_days_per_year\": \"int64\", \"places_to_visit\": \"array<string>\", \"gps_coordinates\": \"struct<latitude float64, longitude float64>\"})\n",
"result[[\"city\", \"is_US_city\", \"polulation_in_millions\", \"rainy_days_per_year\", \"places_to_visit\", \"gps_coordinates\"]]"
"result = gemini.predict(df, prompt=[df[\"city\"]], output_schema={\"is_US_city\": \"bool\", \"population_in_millions\": \"float64\", \"rainy_days_per_year\": \"int64\", \"places_to_visit\": \"array<string>\", \"gps_coordinates\": \"struct<latitude float64, longitude float64>\"})\n",
"result[[\"city\", \"is_US_city\", \"population_in_millions\", \"rainy_days_per_year\", \"places_to_visit\", \"gps_coordinates\"]]"
]
}
],
Expand Down