Pandas is a powerful Python library used for data manipulation and analysis. It allows you to load, clean, and transform data easily into formats that are convenient for analysis or modeling. This blog aims to provide a hands-on guide to using Pandas for essential data manipulation tasks like importing data, selecting columns, filtering rows, grouping data, joining datasets and more. By learning Pandas skills, you will be able to work with real-world datasets more efficiently. These skills are highly useful for anyone pursuing a Python Data Science course in Delhi or looking to build a career in data analysis.
Introduction to Pandas
Pandas is an open-source Python library used for data manipulation and analysis. It is built on the NumPy library and its key feature is that it enables users to store and manipulate data in tabular form similar to a spreadsheet. Data in Pandas is stored and manipulated in specific data structures called Series (1D) and DataFrame (2D). This makes Pandas extremely useful for working with structured or tabular data like data from databases, CSV files etc. In this blog, we will go through the basics of Pandas and see how it can be used to load, clean, transform and visualize data.
Installing Pandas
Pandas is included in the Anaconda distribution of Python which is the easiest way to get started with Pandas. To install Pandas using pip, open a terminal or command prompt and run:
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pip install pandas
This will install the latest stable version of Pandas. You can also install a specific version like pandas==1.2.0. To check if Pandas is installed correctly, open a Python interpreter and import pandas. If there are no errors, it means Pandas is installed properly.
Loading Data into Pandas
There are various ways to load data into Pandas. Some common ways are:
- Load data from CSV/text files:
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import pandas as pd
df = pd.read_csv(‘data.csv’)
- Load data from Excel files:
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df = pd.read_excel(‘data.xlsx’)
- Load data from SQL databases:
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import pandas as pd
import sqlite3
conn = sqlite3.connect(‘data.db’)
df = pd.read_sql_query(“SELECT * FROM table”, conn)
- Load data from JSON files:
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df = pd.read_json(‘data.json’)
- Load data from online API or URL:
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df = pd.read_json(‘https://api.example.com/data’)
Viewing and Inspecting
Data Once data is loaded into a DataFrame, we can view and inspect it. Some common ways are:
- View top/bottom rows:
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df.head()
df.tail()
- Get shape and data types:
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df.shape
df.dtypes
- Get column names/indexes:
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df.columns
df.index
- View summary statistics:
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df.describe()
- View a sample of the data:
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df.sample(5)
This helps get an overview of the data structure and content before proceeding with any manipulations.
Data Manipulation with Pandas
Pandas provides various methods to manipulate the data in DataFrames. Here are some common manipulations:
- Selecting Columns:
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df[[‘col1’,‘col2’]]
- Filtering Rows:
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df[df[‘col’] > 0]
- Sorting values:
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df.sort_values(‘col’)
- Grouping and Aggregation:
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df.groupby(‘col’).sum()
- Joining/Merging DataFrames:
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df1.merge(df2, on=‘key’)
- Reshaping between wide to long format:
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df = df.melt(id_vars=[‘id’], var_name=‘variable’)
- Adding/Removing Columns:
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df[‘new_col’] = df[‘col1’] + df[‘col2’]
df.drop(‘col3’, axis=1)
These are some basic yet powerful manipulations that Pandas enables on DataFrames.
Data Cleaning with Pandas
Real-world data often contains errors, missing values and inconsistencies which need cleaning before analysis. Pandas provides tools to handle these issues:
- Handle missing data:
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df.isnull().sum()
df = df.fillna(0)
- Data type conversions:
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df[‘col’] = df[‘col’].astype(int)
- Remove duplicates:
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df.drop_duplicates()
- Remove outliers:
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q1 = df[‘col’].quantile(0.25)
q3 = df[‘col’].quantile(0.75)
iqr = q3-q1
df = df[(df[‘col’] > (q1-1.5*iqr)) & (df[‘col’] < (q3+1.5*iqr))]
- Handle inconsistent/invalid data:
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df = df[df[‘col’].isin([‘a’,‘b’,‘c’])]
Cleaning data thoroughly is an important step before analysis and modeling to get meaningful insights.
Data Aggregation and Grouping
Pandas provides powerful aggregation functionality using the groupby() method. We can group data by one or more columns and aggregate using common functions like sum, mean, count etc. For example:
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df.groupby(‘category’)[‘sales’].sum()
# Sum of sales for each category
df.groupby([‘year’,‘quarter’])[‘revenue’].mean()
# Average revenue for each year-quarter combination
We can also aggregate on multiple columns at once:
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df.groupby([‘category’,‘city’])[[‘sales’,‘units’]].sum()
This is useful for tasks like finding top products by city, analyzing trends over time etc. Aggregation is an essential part of exploratory data analysis.
Handling Missing Data
Real-world data often contains missing values which need to be handled properly before analysis. Pandas provides various methods to handle missing data:
- Check for missing data:
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df.isnull().sum()
- Fill missing with a value:
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df = df.fillna(0)
- Fill missing forward or backward:
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df[‘col’] = df[‘col’].fillna(method=‘ffill’)
- Drop rows with any missing value:
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df.dropna()
- Impute missing with statistical values:
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from sklearn.impute import SimpleImputer
imputer = SimpleImputer(strategy=‘most_frequent’)
Choosing the right missing data handling technique depends on the nature of missingness and analysis goals. Pandas provides flexibility.
Data Visualization with Pandas Pandas has built-in plotting capabilities and also integrates well with third-party libraries like Matplotlib, Seaborn etc. Some common plots include:
- Line plot:
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df.plot(kind=‘line’, x=‘year’, y=‘sales’)
- Bar plot:
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df.groupby(‘category’)[‘sales’].sum().plot(kind=‘bar’)
- Histogram:
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df[‘col’].plot(kind=‘hist’)
- Scatter plot:
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df.plot(kind=‘scatter’, x=‘x_col’, y=‘y_col’)
- Box and whisker plot:
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df.boxplot(column=[‘col1′,’col2’])
“`1′,’col2′])
“`1′,’col2′])
Visualizations help identify patterns, outliers and get insights from the data in a more intuitive way. Pandas integrates well with Python’s rich ecosystem of visualization libraries.
Advanced Pandas Techniques
Here are some advanced techniques in Pandas:
- Merge/Join DataFrames:
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df1.merge(df2, on=‘key’, how=‘outer’)
- Reshape between long and wide format:
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df = df.set_index([‘id’,‘time’]).unstack()
- Time series functionality:
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df[‘date’] = pd.to_datetime(df[‘date’])
df.resample(‘M’).mean()
- Rolling/Expanding windows:
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df[‘rolling_mean’] = df[‘col’].rolling(7).mean()
- Apply custom functions row-wise or column-wise:
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df[[‘c1’,‘c2’]].apply(lambda x: x[0]*x[1], axis=1)
- Parallelize computations:
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df.groupby(‘col’).transform(func)
Mastering these advanced techniques helps unlock the full power of Pandas for complex data analysis tasks.
Conclusion:
Mastering Data Manipulation with Pandas In this blog, we covered the basics of Pandas including data loading, inspection, cleaning, transformation, aggregation, visualization and some advanced techniques. Pandas provides a rich set of data structures and methods to efficiently manipulate structured data. With its NumPy underpinnings and integration with the Python ecosystem, Pandas has become the most popular tool for data analysis and exploration.