In this pandas tutorial, you’ll learn how to investigate data types within a DataFrame or Series. You’ll also learn how to find and replace entries.
For this exercise we will continue using the famous wine review dataframe. You can obtain this from Kaggle, following this link.
import pandas as pd pd.set_option('max_rows', 5) import numpy as np reviews = pd.read_csv("../input/wine-reviews/winemag-data-130k-v2.csv", index_col=0)
The data type for a column in a DataFrame or a Series is known as the dtype.
You can use the
dtype property to grab the type of a specific column. For instance, we can get the dtype of the
price column in the
dtypes property returns the
dtype of every column in the DataFrame:
country object description object ... variety object winery object Length: 13, dtype: object
Data types tell us something about how pandas is storing the data internally.
float64 means that it’s using a 64-bit floating point number;
int64 means a similarly sized integer instead, and so on.
One peculiarity to keep in mind (and on display very clearly here) is that columns consisting entirely of strings do not get their own type; they are instead given the
It’s possible to convert a column of one type into another wherever such a conversion makes sense by using the
astype() function. For example, we may transform the
points column from its existing
int64 data type into a
float64 data type:
0 87.0 1 87.0 ... 129969 90.0 129970 90.0 Name: points, Length: 129971, dtype: float64
A DataFrame or Series index has its own
Pandas also supports more exotic data types, such as categorical data and timeseries data. Because these data types are more rarely used, we will omit them until a much later section of this tutorial.
Entries missing values are given the value
NaN, short for “Not a Number”. For technical reasons these
NaN values are always of the
Pandas provides some methods specific to missing data. To select
NaN entries you can use
pd.isnull() (or its companion
pd.notnull()). This is meant to be used thusly:
Replacing missing values is a common operation. Pandas provides a really handy method for this problem:
fillna() provides a few different strategies for mitigating such data. For example, we can simply replace each
NaN with an
0 Unknown 1 Unknown ... 129969 Unknown 129970 Unknown Name: region_2, Length: 129971, dtype: object
Or we could fill each missing value with the first non-null value that appears sometime after the given record in the database. This is known as the backfill strategy.
Alternatively, we may have a non-null value that we would like to replace. For example, suppose that since this dataset was published, reviewer Kerin O’Keefe has changed her Twitter handle from
@kerino. One way to reflect this in the dataset is using the
replace() method:In :
0 @kerino 1 @vossroger ... 129969 @vossroger 129970 @vossroger Name: taster_twitter_handle, Length: 129971, dtype: object
replace() method is worth mentioning here because it’s handy for replacing missing data which is given some kind of sentinel value in the dataset: things like
"Invalid", and so on.