<jupyter_start><jupyter_text>Fire up GraphLab Create<jupyter_code>import graphlab as gl
from pandas import Series, DataFrame
import pandas as pd

## Here pandas is working
df = pd.read_csv('people-example.csv')

## GraphLab 
sf = gl.SFrame('people-example.csv')
sf.show()<jupyter_output>PROGRESS: Finished parsing file /home/zax/ml-foundation/people-example.csv
PROGRESS: Parsing completed. Parsed 7 lines in 0.038828 secs.
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Inferred types from first line of file as 
column_type_hints=[str,str,str,int]
If parsing fails due to incorrect types, you can correct
the inferred type list above and pass it to read_csv in
the column_type_hints argument
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PROGRESS: Finished parsing file /home/zax/ml-foundation/people-example.csv
PROGRESS: Parsing completed. Parsed 7 lines in 0.017301 secs.<jupyter_text>Pandas DataFramepeople_data = DataFrame(df)people_data<jupyter_code>myFeatures = ['First Name', 'Last Name', 'age']
df[myFeatures]<jupyter_output><empty_output><jupyter_text>Using canvas<jupyter_code>gl.canvas.set_target('ipynb')
sf.show(view="Scatter Plot", x="Country", y="age")<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.18323746918652423, 'fraction_numerical': 0.01314708299096138, 'mean_word_length': 3.891566265060241, 'pattern_counts': {'":': 0, '<': 12, '<?xml version=': 0, '>': 12, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '324571', 'license': 'MIT', 'n_tokens_mistral': 403, 'n_tokens_neox': 384, 'n_words': 99, 'path': 'ml-foundation/SFrames.ipynb', 'repo_name': 'ledrui/Text-Editor', 'size': '1217'}