Introduction

Set of data in Pandas commonly is in form of multidimensional table named DataFrames. Series such column, DataFrame is entire table. Panda DataFrame is structure of 2-dimensional data such 2-dimensional array or table with row and column.

Locate Row

#refer to the row index:
print(df.loc[0])

#use a list of indexes:
print(df.loc[[0, 1]])

As we can see in the result above, DataFrame such table row and column. Pandas use attribute loc to return one or more determined row.

Load CSV to DataFrame

• The simple way to store set of big data is by using CSV file (Comma Separated Value/file that is delimit by comma).
• CSV files consist of ordinary text and is a format that is well-known for its readability by all people including Panda.
• In the example, we will use CSV file that is called ‘data.csv’

import pandas as pd

df = pd.read_csv('data.csv')

print(df)

Analyze DataFrame

<aside> 💡 One of the methods that is most commonly used to gain brief overview about DataFrame is method of head()

</aside>

Method of **head()**returns header and sum of certain rows from above. Vice versa to view from below then we can use tail() and info() to view the information about dataset.

Load files to Dataframe

<aside> 💡 There are 3 ways to load files.

</aside>

Ask pandas to read.

import pandas as pd
path = '/content/drive/MyDrive/Business Intelligence & Data Scientiest/Dataset/AB_NYC_2019.csv'
df = pd.read_csv(path)
df.describe

<bound method NDFrame.describe of              id                                               name   host_id  \\
0          2539                 Clean & quiet apt home by the park      2787   
1          2595                              Skylit Midtown Castle      2845   
2          3647                THE VILLAGE OF HARLEM....NEW YORK !      4632   
3          3831                    Cozy Entire Floor of Brownstone      4869   
4          5022   Entire Apt: Spacious Studio/Loft by central park      7192   
...         ...                                                ...       ...   
48890  36484665    Charming one bedroom - newly renovated rowhouse   8232441   
48891  36485057      Affordable room in Bushwick/East Williamsburg   6570630   
48892  36485431            Sunny Studio at Historical Neighborhood  23492952   
48893  36485609               43rd St. Time Square-cozy single bed  30985759   
48894  36487245  Trendy duplex in the very heart of Hell's Kitchen  68119814   

           host_name neighbourhood_group       neighbourhood  latitude  \\
0               John            Brooklyn          Kensington  40.64749   
1           Jennifer           Manhattan             Midtown  40.75362   
2          Elisabeth           Manhattan              Harlem  40.80902   
3        LisaRoxanne            Brooklyn        Clinton Hill  40.68514   
4              Laura           Manhattan         East Harlem  40.79851   
...              ...                 ...                 ...       ...   
48890        Sabrina            Brooklyn  Bedford-Stuyvesant  40.67853   
48891        Marisol            Brooklyn            Bushwick  40.70184   
48892  Ilgar & Aysel           Manhattan              Harlem  40.81475   
48893            Taz           Manhattan      Hell's Kitchen  40.75751   
48894     Christophe           Manhattan      Hell's Kitchen  40.76404   

       longitude        room_type  price  minimum_nights  number_of_reviews  \\
0      -73.97237     Private room    149               1                  9   
1      -73.98377  Entire home/apt    225               1                 45   
2      -73.94190     Private room    150               3                  0   
3      -73.95976  Entire home/apt     89               1                270   
4      -73.94399  Entire home/apt     80              10                  9   
...          ...              ...    ...             ...                ...   
48890  -73.94995     Private room     70               2                  0   
48891  -73.93317     Private room     40               4                  0   
48892  -73.94867  Entire home/apt    115              10                  0   
48893  -73.99112      Shared room     55               1                  0   
48894  -73.98933     Private room     90               7                  0   

      last_review  reviews_per_month  calculated_host_listings_count  \\
0      2018-10-19               0.21                               6   
1      2019-05-21               0.38                               2   
2             NaN                NaN                               1   
3      2019-07-05               4.64                               1   
4      2018-11-19               0.10                               1   
...           ...                ...                             ...   
48890         NaN                NaN                               2   
48891         NaN                NaN                               2   
48892         NaN                NaN                               1   
48893         NaN                NaN                               6   
48894         NaN                NaN                               1   

       availability_365  
0                   365  
1                   355  
2                   365  
3                   194  
4                     0  
...                 ...  
48890                 9  
48891                36  
48892                27  
48893                 2  
48894                23  

[48895 rows x 16 columns]>