Pandas Read CSV

Reading CSV Files

Comma-separated values (CSV) files store tabular data as plain text, with each line representing a row and commas separating the columns. The pd.read_csv() function reads a CSV file directly into a DataFrame, automatically inferring column names and data types.

import pandas as pd

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

Handling Large DataFrames With to_string()

When a DataFrame has more rows than the display limit (60 by default), printing it directly shows only the first and last five rows. Calling to_string() forces pandas to print the entire DataFrame instead.

import pandas as pd

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

• sep - the character that separates fields, comma by default
• header - which row to use as the column names
• index_col - a column to use as the row index instead of 0, 1, 2...
• usecols - load only a subset of the available columns
• nrows - limit how many rows are read, useful for huge files

Configuring Pandas Display Options

Instead of calling to_string() every time, you can permanently raise the row limit for your session by changing the max_rows display option before printing.

import pandas as pd

print(pd.options.display.max_rows)
pd.options.display.max_rows = 9999

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