<jupyter_start><jupyter_text>CSV Data Struture<jupyter_code>import pandas as pd
data = pd.read_csv("D:/FOIL2013/trip_data_1/trip_data_1.csv")
data.head()
query = "CREATE TABLE trip_data_11(medallion VARCHAR(20), hack_license VARCHAR(20),)"\
        "vendor_id varchar(10), rate_code INT, store_and_fwd_flag varchar(5), pickup_dataetime datetime"\
        ", dropoff_datetime datetime, passenger_count INT, trip_time_in_secs INT,"\
        "trip_distance DOUBLE,pickup_longitude DECIMAL(20, 10), pickup_latitude DECIMAL(20,10),"\
        "dropoff_longitude DECIMAL(20,10),dropoff_latitude DECIMAL(20,10),pickup_latitude DECIMAL(20,10),"<jupyter_output><empty_output><jupyter_text>Index(['medallion', ' hack_license', ' vendor_id', ' rate_code',       ' store_and_fwd_flag', ' pickup_datetime', ' dropoff_datetime',       ' passenger_count', ' trip_time_in_secs', ' trip_distance',       ' pickup_longitude', ' pickup_latitude', ' dropoff_longitude',       ' dropoff_latitude'],      dtype='object') RangeIndex: 14776615 entries, 0 to 14776614Data columns (total 14 columns):    Column               Dtype  ---  ------               -----   0   medallion            int64   1    hack_license        int64   2    vendor_id           object  3    rate_code           int64   4    store_and_fwd_flag  object  5    pickup_datetime     object  6    dropoff_datetime    object  7    passenger_count     int64   8    trip_time_in_secs   int64   9    trip_distance       float64 10   pickup_longitude    float64 11   pickup_latitude     float64 12   dropoff_longitude   float64 13   dropoff_latitude    float64dtypes: float64(5), int64(5), object(4)memory usage: 1.5+ GB<jupyter_code>my_file = data.head()
my_file.to_csv("sample.csv")
my_file<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10045402951191827, 'fraction_numerical': 0.05732122587968218, 'mean_word_length': 3.615183246073298, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, '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': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '233942', 'license': 'MIT', 'n_tokens_mistral': 659, 'n_tokens_neox': 590, 'n_words': 117, 'path': 'CSV Data Structure.ipynb', 'repo_name': 'vignesh-yaadav/Africar-ai_Car_pooling', 'size': '1762'}