Data wrangling in Python.txt

Importing Libraries

To begin with data wrangling, you will need to import the necessary libraries. The most commonly used library for data wrangling is pandas. You can import pandas using the following code:


import pandas as pd
Loading Data

Before you can start wrangling the data, you will need to load it into a pandas DataFrame. You can do this using the pandas read_csv function, or by loading data from another source such as a database.


# Load data from a CSV file
df = pd.read_csv('data.csv')
Selecting Columns

You can select one or more columns from a DataFrame using the [] operator.


# Select a single column
df['column_name']

# Select multiple columns
df[['column_1', 'column_2']]
Filtering Rows

You can filter the rows of a DataFrame using a boolean mask. To create a boolean mask, you can use comparison operators such as <, >, ==, and != on a column or a combination of columns.


# Select rows where a column is greater than a value
df[df['column'] > value]

# Select rows where multiple conditions are met
df[(df['column_1'] > value_1) & (df['column_2'] == value_2)]
Sorting Data

You can sort the rows of a DataFrame by one or more columns using the sort_values method.


# Sort the DataFrame by a single column
df.sort_values(by='column_name')

# Sort the DataFrame by multiple columns
df.sort_values(by=['column_1', 'column_2'])

# Sort the DataFrame in descending order
df.sort_values(by='column_name', ascending=False)
Grouping and Aggregation

# Grouping and Aggregation

# Group the data by a categorical column and compute the mean of a numerical column
df.groupby('column_1')['column_2'].mean()

# Group the data by multiple columns and compute the mean of a numerical column
df.groupby(['column_1', 'column_2'])['column_3'].mean()

# Apply multiple aggregations to a single column
df.groupby('column_1')['column_2'].agg(['mean', 'min', 'max'])

# Merging and Joining DataFrames

# Merge two DataFrames on a common column
pd.merge(df_1, df_2, on='common_column')

# Merge two DataFrames using an outer join
pd.merge(df_1, df_2, on='common_column', how='outer')

# Concatenating DataFrames

# Concatenate two DataFrames vertically
pd.concat([df_1, df_2])

# Concatenate two DataFrames horizontally
pd.concat([df_1, df_2], axis=1)

# Handling Missing Data

# Drop rows with any missing values
df.dropna()

# Fill missing values with a scalar value
df.fillna(value)

# Fill missing values with the mean of the column
df['column'].fillna(df['column'].mean())