Writing Data
to_csv
pandas
can also write data to a variety of file formats, including CSV, Excel, and SQL databases. The following code cell writes the elections
dataset to a CSV file named elections.csv
.

To write a DataFrame to a CSV file, use the df.to_csv()
function. The first input to df.to_csv()
is the filename or filepath that you want to write to.
'elections_new.csv') pd.to_csv(
Other important parameters of the df.to_csv()
function are:
sep
: (default:sep=','
) specifies the separator used to separate columns. Default is,
which means the columns are separated by a comma.header
: (default:header=True
) specifies whether to write the header row. Default isTrue
which means the header row is written. If you don’t want to write the header row, thenheader=False
should be used.index
: (default:index=True
) specifies whether to write the index column. Default isTrue
which means the index column is written. If you don’t want to write the index column, thenindex=False
should be used. data.to_csv(‘elections.csv’)
Pickle
pickle is a Python module used to serialize and deserialize Python objects. It can be used to store and retrieve Python objects from disk.
Serialization
Serialization is the process of converting a Python object into a byte stream. This byte stream can be stored on disk or sent over a network.
The pickle.dump()
function is used to serialize a Python object. It takes two arguments: the object to serialize and a file object to write the byte stream to.
import pickle
= {'name': 'Alice', 'age': 25}
data
with open('data.pickle', 'wb') as f:
pickle.dump(data, f)
In this example, we serialize a dictionary containing a person’s name and age to a file called data.pickle
.
Deserialization
Deserialization is the process of converting a byte stream back into a Python object.
The pickle.load()
function is used to deserialize a Python object. It takes a file object containing the byte stream as an argument and returns the deserialized object.
with open('data.pickle', 'rb') as f:
= pickle.load(f)
data
print(data)
In this example, we deserialize the byte stream from the data.pickle
file back into a Python object and print it.
Security
It is important to note that the pickle
module is not secure. Deserializing untrusted data can lead to security vulnerabilities, as malicious code can be executed during deserialization. It is recommended to only deserialize data from trusted sources.