<jupyter_start><jupyter_text>Creatimg DataFramesMany ways to do it<jupyter_code>import numpy as np
import pandas as pd

data=np.random.random(size=(5,3))
print(data)
df =pd.DataFrame(data=data, columns=["A","B","C"])
df

df=pd.DataFrame(data={"A":[1,2,3],"B":["Sam","Alex","John"]})
df
dtype=[("A",np.int),("B",(np.str,20))]
data= np.array([(1,"Sam"),(2,"Alex"),(3,"Jhon")],dtype=dtype)
df=pd.DataFrame(data)
df
data= [{"A":1, "B":"Sam"},{"A":2,"B":"Alex"},{"A":3,"B":"Jhon"}]
df=pd.DataFrame(data)
df<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.2937853107344633, 'fraction_numerical': 0.02448210922787194, 'mean_word_length': 11.666666666666666, 'pattern_counts': {'":': 8, '<': 5, '<?xml version=': 0, '>': 5, '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': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '464553', 'license': 'MIT', 'n_tokens_mistral': 242, 'n_tokens_neox': 239, 'n_words': 31, 'path': 'Intro loading/creating_dataframes.ipynb', 'repo_name': 'awes0m/pandaconda', 'size': '531'}