Pandas Quick Start Guide

What is Pandas?

Pandas is a Python library that used to read, clean, visualize, and process a dataset. Pandas uses a high-performance data analysis functions to manipulate data a structures  which called data frame. You can compare this to a table in tools like Excel. Learn Python with Codecademy. Master the skill that your future employer is looking for. Sign up for Codecademy Pro today and save 50% on an annual plan!

How to Install Pandas?

To install pandas all that you need to install Python. If you havent, check out the link here Once installed, use pip to install Pandas like that. If the alert  “Requirement already satisfied”  is triggered then Pandas already installed in your device.

In [1]:
!pip install pandas 
Requirement already satisfied: pandas in /srv/conda/envs/notebook/lib/python3.7/site-packages (0.24.2)
Requirement already satisfied: pytz>=2011k in /srv/conda/envs/notebook/lib/python3.7/site-packages (from pandas) (2019.1)
Requirement already satisfied: numpy>=1.12.0 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from pandas) (1.16.4)
Requirement already satisfied: python-dateutil>=2.5.0 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from pandas) (2.8.0)
Requirement already satisfied: six>=1.5 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from python-dateutil>=2.5.0->pandas) (1.12.0)

Importing Pandas library.

You need to import the library using the “import ” command combined with the name of the library. Typically, you can add an alias to the library name for simplicity like “import pandas as pd”. Now, the alias can be used in place of the function name. For example, which is “pd.function_name()”.

In [2]:
import pandas as pd
import numpy as np
import warnings

Reading the data using Pandas DataFrame

Pandas uses a high-performance data structure to store the data in it. This “DataFrame” is a two dimension which can handle data from different formats like json, xlxs, CSV, and much more. The data frame is  similar in structure to tables in Database Management Systems (DBMS).  To read the dataset in the data frame a read function is used. For example,  read_csv() function if your data in CSV format or read_json(), read_excel(), read_hdf() etc. Typically it follows according to the data format.

In [3]:
#csv_data = pd.read_csv("file_name.csv")
#excel_data = pd.read_excel("file_name.excel")
#HtMl_data = pd.read_html("file_name.html")

Build a Dummy DataFrame using python Dictionary

Before working on a real-life dataset sometimes we need to build a dummy data frame to try in it before applying the methods to the real DataFrame to do this all that we need is to use Dataframe() function in pandas and give it the dictionary as we can see in the snippet code below.

In [4]:
data_dict = {"id":[1,2,3,4,5,6,7,8,9], 
df = pd.DataFrame(data_dict)
In [5]:

Cleaning the Dataset using Pandas

First, we need to define what we mean by “cleaning the Data”. All the real-world datasets have problems like missing in data, incorrect naming of features, inappropriate values of the feature like time in minus, The insufficient number of features and much more. So, we need to clean this dataset to be able to start using it. Pandas and DataFrames have a numerous number of functions for handling these problems in data like isna() and isnull() functions in Pandas which check the existence of missing values and dropna(), fillna() which handle these missing variables in the dataset. So, we can choose between to drop these missing values or fill it with an appropriate value like the median of the values of the feature that has this missing.

In [6]:
id        False
age        True
sex       False
Status    False
Salary     True
dtype: bool

Here we used fillna() function to fill the missing value in this salary column with the value ’70k’.

Notice : We can use the median value of the feature to fill the missing values in these features. But the feature values must be numerical to be able to calculate this fea

In [7]:
0    100k
1     50k
2     60k
3    120k
4     90k
5     70k
6     30k
7    160k
8    200k
Name: Salary, dtype: object

The second way to handle the missing values is to drop any rows have any missing in the data using dropna() function. like the way below.

In [8]:
df = df.dropna()

As we can notice in the DataFrame below the index value is not sequenced after dropping the row that was had missing. So, to make the index sequenced like 1,2,3,4,5, etc. We need to use reset_index() function.

In [9]:

Replacing the String or object column to a numerical

As we have mentioned above we need to fill the missing values with the Median Salary. So, we need to convert the String column into a numerical column. But the process is not straightforward because we cannot convert the column directly because of the existence of ‘k’ that represents 1000. So, we need to convert the k first to 000 this can be handled easily using replace() function as below.

In [10]:
df['Salary'] =  df['Salary'].str.replace('k','000')
In [11]:
0    100000
1     50000
3    120000
4     90000
6     30000
7    160000
8    200000
Name: Salary, dtype: object

Now, we can easily convert the string into numerical using astype() function.

Hint: astype() function takes one parameter that indicates the type which we want to convert to it like that astype(float) that convert the column into the float.

In [12]:
df['Salary'] = df['Salary'].astype(float)
In [13]:
df = df.reset_index()

Now, we can fill the missing values of the salary column with the median value of the feature using the fillna() function we mentioned before.

In [14]:
df['Salary'] = df['Salary'].fillna(df['Salary'].median())

Dropping Columns

There many cases we need to drop a column in the data that represent redundancy in our data or if we implement the machine learning model we need to drop the target feature to train the model on it. So, Dropping the column from DataFrame is an essential task in cleaning the data. So, in pandas, we have two ways of dropping the column.

1- Using drop() function this way the DataFrame make a copy from the original DataFrame without the dropped column

2- The Second way is to use ‘del’ to remove the column and this removes the column permanently from the DataFrame and we will need to read the data again from the CSV file to restore the column.

In [15]:

As we can see in the DataFrame above we have a redundant column called index result from applying reset_index(). So, we need to drop this column. The first method we use drop() function as below.

Hint: We need to specify the axis we want to drop from it

axis = 0 ->> represent rows

axis = 1 ->> represent columns

In [16]:
data_copy = df.drop('index', axis = 1)

Now, data_copy has a copy of the df without the index column as we can see below.

Hint head() function display the first 5 rows of the data. We can determine the number of the displayed column by sending the number to the function as a parameter.

In [17]:

df Dataframe still as it the drop function didn’t impact it. As we can see below the index column still exist.

In [18]:

Now, we will try using del to remove the index column from the df Dataframe.

In [19]:
del df['index']

As we can see in the DataFrame below the index column has removed from our original DataFrame (df).

In [20]:

Inserting columns to DataFrame

The process of adding columns is an important task in Data Handling and it simple task. All we need to do is to define the name of the new column and assign the list of the values to this name as the below.

In [21]:
 df['Data_Of_Birth'] = ['01-05-1998', '10-09-1985', '08-02-1994','09-08-1969','02-02-1985','02-09-1999','11-11-1975']
In [22]:

Now, we want to check the data types of Columns in DataFrame using dtypes and if we found the Data_Of_Birth column has not the date type we will convert it using astype(). As we can see the data type of column Date_Of_Brith is object so we need to convert it to a date data type.

In [23]:
id                 int64
age              float64
sex               object
Status            object
Salary           float64
Data_Of_Birth     object
dtype: object
In [24]:
df['Data_Of_Birth'] = df['Data_Of_Birth'].astype('datetime64[ns]')

Working with date columns

Now, we can find that the column data type converted into the date.

In [25]:
id                        int64
age                     float64
sex                      object
Status                   object
Salary                  float64
Data_Of_Birth    datetime64[ns]
dtype: object
In [26]:

We can get the name of the day of the week from the date column using dt.dayofweek function. This function returns the days of the week as numerical values like 0, 1, 2, 3, 4, 5, 6. So, we need to map each value of these numbers into the name of weekdays.

In [27]:
dayOfWeek={0:'Monday', 1:'Tuesday', 2:'Wednesday', 3:'Thursday', 4:'Friday', 5:'Saturday', 6:'Sunday'}
df['weekday'] = df['Data_Of_Birth']

Here we have generated the weekday column from the date column and in the same way we can split the data column into a number of features.

In [28]:

Slicing the DataFrame using loc and iloc functions

Selecting specific rows from the data frame is one of the most important tasks as well. This used to be able to choose specific rows based on the index or based on the positions. First, we need to explain the difference between the two functions loc and iloc.


iloc is used to slice the data frame based on the position that you specify. The square brackets with the iloc[] function allow you to select columns and rows. The comma separates the the colum and rows and a colon allows youto select between the columns or rows.

For example

df.iloc [:4] this means you want the first four rows regardless the index.

df.iloc[-4:] this means you want to get the last four rows of the data.

df.iloc[:3 , :3] this means you want to get the first 3×3 matrix of the data.


2- loc: loc depends on the index and columns names in slicing the DataFrame for Example if  “weekend”  was the last column.  You can get all the rows for the last columns by using loc as follows df.loc[:, ‘weekday’] whereas the same task can be achieved using iloc like that df.iloc(:,6). Also if our index are like the follow a,b,c,d,etc, to access the first row via loc we need to do that df.loc[‘a’] but using iloc we can do this as df.iloc[0]. Let’s try this using coding.

In [29]:
df.iloc[:3 , :3]

Here we can find that df.iloc[-4:] return the same results as df.loc[3:6] because df.loc[3:6] get the rows starting from index 3 to the index 6 and this the same we got using df.iloc[-4:] that return the last four rows. I hope the difference is clear now.

In [30]:
In [31]:
In [32]:
 df.loc[:, 'weekday']
0       Monday
1    Wednesday
2      Tuesday
3       Monday
4     Saturday
5      Tuesday
6      Tuesday
Name: weekday, dtype: object

Gaelim Holland

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