To fix UnicodeDecodeError when reading CSV file in Pandas with Python, we call read_csv with engine set to 'python'.
For instance, we write
import pandas as pd
df = pd.read_csv('file_name.csv', engine='python')
to call read_csv with the file path of the CSV and engine set to 'python' to use the Python native CSV parser to parse the CSV.
To avoid Python Pandas creating an index in a saved CSV, we set the index option to false.
For instance, we write`
df.to_csv('your.csv', index=False)
to call to_csv with the file path and the index argument set to False to avoid adding the index in the CSV.
To check if any value is NaN in a Python Pandas DataFrame, we use the iosnull and any methods.
For instance, we write
df.isnull().values.any()
to check if any values are NaNin our Pandas data framedf`.
To convert a Python Pandas GroupBy output from Series to DataFrame, we can use count.
For instance, we write
df1.groupby(["Name", "City"])[['Name','City']].count()
to call groupby with count to return the groupby result as a data frame.
To convert Python Pandas dataframe to NumPy array, we can use the to_numpy method.
For instance, we write
df = pd.DataFrame(data={'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]},
index=['a', 'b', 'c'])
n = df.to_numpy()
to create the df data frame with some data in it.
Then we call df.to_numpy to return the df data frame as a NumPy object.