You asked for it, so here it is the ultimate Python data wrangling cheat sheet.
This handy guide will help you navigate through some of the most common data manipulation tasks in Python. From cleaning and transforming your data to visualizing and analyzing it, we’ve got you covered!
So grab a cup of coffee (or tea if that’s more your style), sit back, relax, and let’s get started! ️
1. Loading Data:
To load data from CSV files or Excel sheets, you can use the pandas library. Here are some examples to help you out:
# Import the pandas library and assign it an alias "pd"
import pandas as pd
# Load a CSV file using the read_csv() function from pandas
# Assign the loaded data to a variable "df"
df = pd.read_csv('data.csv')
# Load an Excel sheet using the ExcelFile() function from pandas
# Assign the loaded data to a variable "excel_file"
excel_file = 'my_sheet.xls'
# Parse the Excel sheet using the parse() function from pandas
# Specify the sheet name as "Sheet1" and assign the parsed data to a variable "df"
df = pd.ExcelFile(excel_file).parse('Sheet1')
2. Cleaning Data:
Cleaning data is a crucial step in any data wrangling process. Here are some common techniques to help you get started:
– Drop rows with missing values (NaN): `df = df.dropna()`
– Remove duplicates: `df = df.drop_duplicates()` or `df = df.drop_duplicates(subset=[‘column1’, ‘column2’])` to remove duplicates based on specific columns
– Convert categorical data to numerical: `df[‘category’] = pd.factorize(df[‘category’])[0]`
– Remove whitespace from strings: `df[‘string_col’] = df[‘string_col’].str.strip()`
3. Transforming Data:
Transforming data involves changing the structure or format of your data to better suit your needs. Here are some common techniques:
– Pivot tables (reshaping): Use the `pivot_table()` function from pandas to create pivot tables and summarize your data by different categories. For example, let’s say you have a dataset with sales figures for each product in each region. You can use the following code to create a summary table that shows total sales per region:
# Import the pandas library
import pandas as pd
# Read the csv file and store it in a dataframe called df
df = pd.read_csv('data.csv')
# Use the pivot_table() function to create a summary table
# The index parameter specifies the column to use as the index for the table
# The columns parameter specifies the column to use as the columns for the table
# The values parameter specifies the column to use as the values for the table
# The aggfunc parameter specifies the function to use for aggregating the values
# In this case, we use np.sum to calculate the total sales for each region and product
pivot_table = df.pivot_table(index='region', columns='product', values='sales', aggfunc=np.sum)
# Print the pivot table
print(pivot_table)
– Grouping data: Use the `groupby()` function from pandas to group your data by different categories and perform aggregate functions on each group. For example, let’s say you have a dataset with sales figures for each product in each region. You can use the following code to calculate the average sale price per product:
# Import the pandas library
import pandas as pd
# Read the data from a csv file and store it in a dataframe called 'df'
df = pd.read_csv('data.csv')
# Use the groupby() function to group the data by the 'product' column and calculate the mean of the 'price' column for each group
grouped = df.groupby(['product'])['price'].mean()
# Print the result, which is a series object with the average sale price for each product
print(grouped)
4. Visualizing Data:
Visualization is a powerful tool for exploring and understanding your data. Here are some common techniques to help you get started:
– Scatter plots: Use the `scatter()` function from matplotlib to create scatter plots that show the relationship between two variables. For example, let’s say you have a dataset with sales figures for each product in each region. You can use the following code to create a scatter plot that shows the relationship between sales and price:
# Import the necessary library for creating scatter plots
import matplotlib.pyplot as plt
# Read the data from a csv file and store it in a dataframe
df = pd.read_csv('data.csv')
# Create a scatter plot using the 'price' column as the x-axis and the 'sales' column as the y-axis
plt.scatter(df['price'], df['sales'])
# Display the scatter plot
plt.show()
– Histograms: Use the `hist()` function from matplotlib to create histograms that show the distribution of your data. For example, let’s say you have a dataset with sales figures for each product in each region. You can use the following code to create a histogram that shows the distribution of sales:
# Import the necessary libraries
import matplotlib.pyplot as plt # Importing the pyplot module from the matplotlib library
import pandas as pd # Importing the pandas library and assigning it an alias of 'pd'
# Read the data from the csv file
df = pd.read_csv('data.csv') # Using the read_csv() function from the pandas library to read the data from the csv file and assigning it to the variable 'df'
# Create a histogram using the sales data from the dataframe
plt.hist(df['sales']) # Using the hist() function from the pyplot module to create a histogram of the 'sales' column from the dataframe 'df'
plt.show() # Displaying the histogram on the screen
5. Analyzing Data:
Analyzing data involves using statistical techniques to identify trends and patterns in your data. Here are some common techniques to help you get started:
– Correlation analysis: Use the `corr()` function from pandas or NumPy to calculate correlation coefficients between different variables. For example, let’s say you have a dataset with sales figures for each product in each region. You can use the following code to calculate the correlation coefficient between sales and price:
# Import the necessary libraries
import pandas as pd # Import pandas library and assign it an alias "pd"
import numpy as np # Import numpy library and assign it an alias "np"
# Read the data from a CSV file and store it in a dataframe
df = pd.read_csv('data.csv') # Use the read_csv() function from pandas to read the data from a CSV file and store it in a dataframe called "df"
# Calculate the correlation coefficient between two variables
corr = df[['price', 'sales']].corr() # Use the corr() function from pandas to calculate the correlation coefficient between the "price" and "sales" columns in the dataframe "df" and store it in a variable called "corr"
# Print the correlation coefficient
print(corr) # Use the print() function to display the correlation coefficient calculated in the previous step
– Regression analysis: Use the `ols()` function from StatsModels to perform linear regression on your data. For example, let’s say you have a dataset with sales figures for each product in each region. You can use the following code to perform linear regression between sales and price:
# Import the necessary library for performing linear regression
import statsmodels.api as sm
# Read the data from a csv file and store it in a dataframe
df = pd.read_csv('data.csv')
# Select the independent variable (price) and store it in a variable X
X = df[['price']]
# Select the dependent variable (sales) and store it in a variable y
y = df['sales']
# Create a linear regression model using the OLS function from StatsModels
model = sm.OLS(y, X)
# Fit the model to the data and store the results in a variable
results = model.fit()
# Print a summary of the regression results
print(results.summary())
And there you have it the ultimate Python data wrangling cheat sheet! We hope this guide has helped you navigate through some of the most common data manipulation tasks in Python. Remember, practice makes perfect, so keep experimenting and exploring with your data!