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First steps with pandas (Python): importing datasets and how they look

Recently I started to learn Python and, especially, how to manage datasets with it. pandas is one of the well-known libraries for data management in Python. So, I would like to share my notes and my first steps with pandas.

First, I import the library with the name pd:

import pandas as pd

One of the first things I learned in Python is that libraries can be labeled/named as one wants. This is useful to be more efficient when writng code. Since I have labeled pandas as pd, now it is enough to write pd to call the library, and it is no longer necessary to write pandas. Labeling the libraries is not mandatory at all, and I could have basically imported the library as import pandas. In this case, I should have called pandas as pandas.

I mainly focus on csv and excel files in this post. I concretely import an example excel file from this github website. For that, I create an object called dt, which is a data frame. Then, I indicate to Python that I want to use pandas library to import the file and the corresponding command: read_excel().

dt = pd.read_excel('https://file-examples-com.github.io/uploads/2017/02/file_example_XLS_50.xls')

In case of importing a csv file, the command is read_csv. For example, I import a csv file from the New Zeleand Government website:

dt2 = pd.read_csv('https://www.stats.govt.nz/assets/Uploads/Annual-enterprise-survey/Annual-enterprise-survey-2019-financial-year-provisional/Download-data/annual-enterprise-survey-2019-financial-year-provisional-csv.csv')

However, to import other data files formats such as SAS, SPSS, STATA, JSON, or SQL, among others, the function would be:

  • .read_sas()
  • .read_spss()
  • .read_stata()
  • .read_sql()
  • .read_json()
  • .read_table()

Hereafter, I only want to use the dt dataset. For that reason, I delete the dt2 database with the command del():

del(dt2)

Now that the dataset is loaded in the environtment, I want to check how it looks.

The command .head() shows the first 5 observations (rows) of the data frame and all the variables (columns). This command is similar to head() in R. Remember that before the command, .head(), we must indicate the data frame we want to look at:

dt.head()

Moreover, the number of observations to be visualized can be indicated. For example, the first 8 observations:

dt.head(8)

On the other hand, the command .tail() shows the last 5 observations or, similarly, .tail(n) the last ‘n’ observations.

dt.tail()
dt.tail(9)

However, one might be interested in looking the observations of a specific variable such as:

dt["Gender"].head()
dt["Country"].head()

In order to retrieve some information about the data frame and its structure:

dt.info()
len(dt)

Now, I’m interested in the main descriptive statistics of the numerical variables from the dataset:

dt.describe()

Note that in case of not specifying any variable the code will show all the numerical variables from the data frame. However, if I want to check a specific variable:

dt["Age"].describe()

This function is the same than tabulate (for Stata users) and count() or table()(for R users).

dt['Age'].value_counts()
dt['Age'].nunique()

To check the number of missing values for each variable better to add .sum() at the end:

dt.isna().sum()

Without the .sum() function, a false/true matrix is shown, indicating whether every single value of the data frame is missing or not.

This function is similar than sort (for Stata users) and arrange() (for R users). It orders observations from low to high values (ascending):

dt['Age'].sort_values()
dt.sort_values('Age')

For descending order:

dt['Age'].sort_values(ascending=False)
dt.sort_values('Age', ascending=False)

In the next post on pandas I will show how to perform the first steps on data wrangling: rename variable, create new ones, filter observations, merge datasets…

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