When working with data in Python, it is common to have multiple related data frames that you need to apply operations and analysis across. For example, you may have separate data frames for different years or categories that you want to combine and compare. Applying functions and transformations across multiple data frames requires some careful handling, but Python provides several powerful tools to make this process efficient and straightforward.
In this comprehensive guide, we will explore methods for applying operations and analysis across multiple related data frames using Python and its key data analysis libraries like Pandas, NumPy, and SciPy.
Table of Contents
Open Table of Contents
Overview
A data frame is a two-dimensional tabular data structure with labeled columns and rows, similar to a spreadsheet. The Pandas library in Python provides excellent capabilities for working with data frames for data cleaning, preprocessing, and analysis.
When working with multiple related data frames, such as those split by year or category, we need to apply the same operations and analysis across all the frames in a scalable way while handling indices and labels appropriately.
Key topics we will cover include:
- Concatenating multiple data frames into a single combined data frame
- Applying functions to multiple data frames using loop structures
- Leveraging Pandas methods to analyze and operate on groups of data frames
- Using aggregation functions to combine data frame statistics and metrics
- Merging and joining data frames to link data across multiple frames
- Concatenating data frames with inner, outer, left, and right joins
Understanding these techniques will allow you to efficiently apply analysis across large, complex datasets with many interrelated data frames in Python.
Importing Key Libraries
We will utilize the following core Python libraries for working with multiple data frames:
# Import Pandas for data frame operations
import pandas as pd
# Import NumPy for numerical operations on array data
import numpy as np
# Import SciPy for statistical tests and analyses
from scipy import stats
Pandas provides the DataFrame object and associated methods while NumPy enables array-based computations. SciPy contains statistical tests we can apply across data frames.
Concatenating Data Frames
Concatenation joins multiple data frames together into a larger combined data frame. This is useful when you have data split across multiple frames and need to bring it together for analysis.
It’s important to note that concatenation does not modify the original data frames. Instead, it returns a new concatenated data frame.
Pandas provides two main methods for concatenating data frames:
concat()- Joins data frames by row or column into a new combined data frame.
df_concat = pd.concat([df1, df2])
append()- Appends rows of one data frame to the end of another.
df_concat = df1.append(df2)
Both achieve the same result of concatenating df1 and df2.
By default they append rows, but you can specify axis=1 to concatenate by columns instead. You can pass any number of data frames in a list to concatenate together.
Set the ignore_index=True parameter to avoid duplicate indices when concatenating. You can also add keys to identify the source data frames:
df_concat = pd.concat([df1, df2], keys=['x', 'y'])
This labels each data frame with an identifying key for reference.
Applying Functions to Multiple Data Frames
To apply the same operation to multiple data frames, such as filtering or transforming values, you can use a for loop to iterate over each data frame.
It’s important to note that the operations within the loop do not modify the original data frames. To keep the results, you need to store the filtered data frames.
# Import Pandas for data frame operations
import pandas as pd
# Create sample data frames
data1 = {'Name': ['Alice', 'Bob', 'Charlie'],
'Age': [25, 30, 35]}
data2 = {'Name': ['David', 'Eve', 'Frank'],
'Age': [22, 28, 40]}
df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)
# List of original data frames
df_list = [df1, df2]
# Empty list to hold filtered data frames
df_filtered = []
# Apply filtering to each data frame
for df in df_list:
filtered_df = df[df['Age'] > 30] # Example filtering condition
df_filtered.append(filtered_df)
# Display filtered data frames
for i, df in enumerate(df_filtered):
print(f"Filtered Data Frame {i + 1}:")
print(df)
This prints:
Filtered Data Frame 1:
Name Age
2 Charlie 35
Filtered Data Frame 2:
Name Age
2 Frank 40
This loops through and filters each data frame based on a criteria, storing the filtered versions in a new list.
More complex functions can be applied as well within the loop besides filtering.
Using GroupBy on Multi-Frames
Pandas GroupBy allows you to group data frames by a category column and apply operations within each group. This can be used across multiple data frames:
# Import Pandas for data frame operations
import pandas as pd
# Create sample data frames
data1 = {'Category': ['A', 'B', 'A', 'B'],
'Value': [10, 15, 20, 25]}
data2 = {'Category': ['A', 'B', 'A', 'B'],
'Value': [30, 35, 40, 45]}
df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)
# List of data frames
dfs = [df1, df2]
# Group each data frame by 'Category' column
grouped = [df.groupby('Category') for df in dfs]
# Apply aggregation to each group
df_means = [group.mean() for group in grouped]
# Concatenate the group means into a single data frame
result_df = pd.concat(df_means, keys=['df1', 'df2'])
# Display the result data frame
print("Resulting Data Frame:")
print(result_df)
This prints:
Resulting Data Frame:
Value
Category
df1 A 15.0
B 20.0
df2 A 35.0
B 40.0
This groups each data frame by ‘category’, applies the .mean() aggregation to each group, and stores the results into df_means, which is a list containing a data frame of group means for each original data frame in dfs.
Grouping multiple data frames results in a hierarchical index. The index levels can be reset with df_means[0].reset_index() if needed.
To concatenate the group means into a single data frame, you can use pd.concat(df_means).
Any aggregate operations like sum(), count(), max() etc. can be applied across the grouped data frames.
Joining and Merging Data Frames
Joining or merging allows you to combine data from different data frames. This is helpful when data is split across multiple data frames but needs to be linked.
Pandas provides two main functions for joining:
-
merge()- SQL-style joins on columns or indices. -
join()- Joins strictly on indices.
The merge() function handles all types of joins:
# Import Pandas for data frame operations
import pandas as pd
# Create sample data frames
data1 = {'ID': [1, 2, 3],
'Name': ['Alice', 'Bob', 'Charlie']}
data2 = {'ID': [2, 3, 4],
'Age': [25, 30, 35]}
df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)
# Merge df1 and df2 on the 'ID' column
df_merged = pd.merge(df1, df2, on='ID')
# Left join keeps all rows of df1
df_left = pd.merge(df1, df2, on='ID', how='left')
# Right join keeps all rows of df2
df_right = pd.merge(df1, df2, on='ID', how='right')
# Outer join keeps all rows of both
df_outer = pd.merge(df1, df2, on='ID', how='outer')
# Join on index
df1.set_index('ID', inplace=True)
df2.set_index('ID', inplace=True)
df_idx = df1.join(df2, how='inner')
# Display the merged and joined data frames
print("Merged Data Frame:")
print(df_merged)
print("\nLeft Join Data Frame:")
print(df_left)
print("\nRight Join Data Frame:")
print(df_right)
print("\nOuter Join Data Frame:")
print(df_outer)
print("\nJoined Data Frame on Index:")
print(df_idx)
This prints:
Merged Data Frame:
ID Name Age
0 2 Bob 25
1 3 Charlie 30
Left Join Data Frame:
ID Name Age
0 1 Alice NaN
1 2 Bob 25.0
2 3 Charlie 30.0
Right Join Data Frame:
ID Name Age
0 2 Bob 25
1 3 Charlie 30
2 4 NaN 35
Outer Join Data Frame:
ID Name Age
0 1 Alice NaN
1 2 Bob 25.0
2 3 Charlie 30.0
3 4 NaN 35.0
Joined Data Frame on Index:
Name Age
ID
2 Bob 25
3 Charlie 30
For index-only joins, use df1.join(df2):
# Join on index
df1.join(df2, how='inner')
You can join multiple data frames by passing a list:
df_joined = df1.join([df2, df3, df4], how='inner')
This performs an inner join across all the data frames.
Combining Data Frame Statistics
When working with multiple data frames, you may need to combine or compare summary statistics across the frames.
For example, calculating descriptive stats for each frame and aggregating together:
# Import Pandas for data frame operations
import pandas as pd
# Create sample data frames
data1 = {'ID': [1, 2, 3, 4],
'Value1': [10, 15, 20, 25]}
data2 = {'ID': [1, 2, 3, 4],
'Value2': [30, 35, 40, 45]}
df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)
# List of data frames
df_list = [df1, df2]
# Empty list to hold summary statistics data frames
stats_list = []
# Calculate summary stats for each data frame
for df in df_list:
df_stats = df.describe()
stats_list.append(df_stats)
# Concatenate stats horizontally into one data frame
stats_df = pd.concat(stats_list, axis=1)
# Display the combined summary statistics data frame
print("Combined Summary Statistics Data Frame:")
print(stats_df)
This prints:
Combined Summary Statistics Data Frame:
ID Value1 ID Value2
count 4.000000 4.000000 4.000000 4.000000
mean 2.500000 17.500000 2.500000 37.500000
std 1.290994 6.454972 1.290994 6.454972
min 1.000000 10.000000 1.000000 30.000000
25% 1.750000 13.750000 1.750000 33.750000
50% 2.500000 17.500000 2.500000 37.500000
75% 3.250000 21.250000 3.250000 41.250000
max 4.000000 25.000000 4.000000 45.000000
This calculates the describe() statistics for each data frame and concatenates the results horizontally into stats_df, with columns corresponding to each original data frame.
You can also use aggregation functions like pandas.concat() and pandas.merge() to combine group statistics:
# Group by category
grouped = df.groupby('category')
# Compute mean of each group
means = grouped.mean()
# Compute sum of each group
sums = grouped.sum()
# Merge the two DFs
stats = means.merge(sums, on='category')
This aggregates the data into separate means and sums data frames then merges them into a final stats data frame.
Applying Statistical Tests Across Data Frames
Python’s SciPy library provides statistical tests that can be applied across multiple data frames.
It’s essential to check test assumptions and ensure the data frames are compatible before applying statistical tests.
For example, performing a two sample t-test to compare values between two data frames:
# Import SciPy for statistical tests and analyses
from scipy import stats
# Example data frames df1 and df2
# Make sure to replace these with your actual data frames
df1 = pd.DataFrame({'values': [1.2, 2.5, 3.8, 4.1, 5.3]})
df2 = pd.DataFrame({'values': [2.0, 2.7, 3.6, 4.0, 5.1]})
# Extract columns from each DF
vec1 = df1['values']
vec2 = df2['values']
# Check vectors are equal length
assert len(vec1) == len(vec2)
# Perform a two-sample t-test assuming normality
t_stat, p_val = stats.ttest_ind(vec1, vec2)
# Print p-value
print("P-value:", p_val)
# Interpret the results
if p_val < 0.05:
print("Reject the null hypothesis: There is a significant difference between the two data sets.")
else:
print("Fail to reject the null hypothesis: There is no significant difference between the two data sets.")
This prints:
P-value: 0.9126844168523223
Fail to reject the null hypothesis: There is no significant difference between the two data sets.
This code snippet demonstrates how to perform a two-sample t-test between two data frames (df1 and df2) assuming that the data follows a normal distribution. It checks the equality of means between the two data sets and prints the p-value. You can replace df1 and df2 with your actual data frames and adapt the test as needed based on your specific requirements.
Tests like ANOVA, correlations, chi-square, and others can be applied across properly formatted data frames.
Case Study - Analyzing Store Sales Data Frames
Let’s walk through an applied example of using these techniques to analyze daily sales data frames from different store locations.
First, let’s import Pandas and create dummy sales data frames for store locations 1 to 4:
import pandas as pd
# Dummy sales data frames
sales_data = {
'store_id': [1, 2, 3, 4],
'date': ['2023-01-01', '2023-01-01', '2023-01-01', '2023-01-01'],
'customer_id': [101, 102, 103, 104],
'amount': [50.0, 75.0, 60.0, 90.0]
}
# Create sales data frames for each store
sales_dfs = []
for store_id in [1, 2, 3, 4]:
df = pd.DataFrame(sales_data)
df['store_id'] = store_id
sales_dfs.append(df)
# Concatenate sales data frames into one
all_stores = pd.concat(sales_dfs, ignore_index=True)
We have created dummy sales data frames for each store location and concatenated them into a single data frame all_stores.
Next, let’s create a dummy customer demographics data frame:
# Dummy customer demographics data
customers_data = {
'customer_id': [101, 102, 103, 104],
'age': [30, 25, 35, 40],
'gender': ['M', 'F', 'M', 'F']
}
# Create customer demographics data frame
customers_df = pd.DataFrame(customers_data)
Now, let’s merge the sales data with the customer demographics data:
# Merge sales data with customer demographics data
sales_with_cust = pd.merge(all_stores, customers_df, on='customer_id')
The sales_with_cust data frame now contains both sales data and customer demographics data merged on the ‘customer_id’ column.
Next, we can analyze sales by customer segment across all stores. For example, let’s calculate the total sales amount by gender:
# Analyze sales by customer segment across all stores
segment_stats = sales_with_cust.groupby('gender').agg({'amount': 'sum'})
# Display sales by customer segment
print("Sales by Customer Segment:")
print(segment_stats)
This code calculates the total sales amount for each gender and displays the results.
Complete Code Example:
# Import Pandas for data frame operations
import pandas as pd
# Dummy sales data frames
sales_data = {
'store_id': [1, 2, 3, 4],
'date': ['2023-01-01', '2023-01-01', '2023-01-01', '2023-01-01'],
'customer_id': [101, 102, 103, 104],
'amount': [50.0, 75.0, 60.0, 90.0]
}
# Create sales data frames for each store
sales_dfs = []
for store_id in [1, 2, 3, 4]:
df = pd.DataFrame(sales_data)
df['store_id'] = store_id
sales_dfs.append(df)
# Concatenate sales data frames into one
all_stores = pd.concat(sales_dfs, ignore_index=True)
# Group the data by store location and calculate statistics
stats_by_store = all_stores.groupby('store_id').agg({'amount': ['sum', 'mean', 'max']})
# Display statistics by store
print("Statistics by Store:")
print(stats_by_store)
# Dummy customer demographics data
customers_data = {
'customer_id': [101, 102, 103, 104],
'age': [30, 25, 35, 40],
'gender': ['M', 'F', 'M', 'F']
}
# Create customer demographics data frame
customers_df = pd.DataFrame(customers_data)
# Merge sales data with customer demographics data
sales_with_cust = pd.merge(all_stores, customers_df, on='customer_id')
# Display merged data
print("\nSales Data with Customer Demographics:")
print(sales_with_cust)
# Analyze sales by customer segment across all stores
segment_stats = sales_with_cust.groupby('gender').agg({'amount': 'sum'})
# Display sales by customer segment
print("\nSales by Customer Segment:")
print(segment_stats)
This prints:
Statistics by Store:
amount
sum mean max
store_id
1 275.0 68.75 90.0
2 275.0 68.75 90.0
3 275.0 68.75 90.0
4 275.0 68.75 90.0
Sales Data with Customer Demographics:
store_id date customer_id amount age gender
0 1 2023-01-01 101 50.0 30 M
1 2 2023-01-01 101 50.0 30 M
2 3 2023-01-01 101 50.0 30 M
3 4 2023-01-01 101 50.0 30 M
4 1 2023-01-01 102 75.0 25 F
5 2 2023-01-01 102 75.0 25 F
6 3 2023-01-01 102 75.0 25 F
7 4 2023-01-01 102 75.0 25 F
8 1 2023-01-01 103 60.0 35 M
9 2 2023-01-01 103 60.0 35 M
10 3 2023-01-01 103 60.0 35 M
11 4 2023-01-01 103 60.0 35 M
12 1 2023-01-01 104 90.0 40 F
13 2 2023-01-01 104 90.0 40 F
14 3 2023-01-01 104 90.0 40 F
15 4 2023-01-01 104 90.0 40 F
Sales by Customer Segment:
amount
gender
F 660.0
M 440.0
These examples demonstrate applying operations and analysis to the multiple related sales data frames using Python and Pandas.
Summary
In this comprehensive guide we explored practical techniques for applying transformations and analysis across multiple related data frames in Python, including:
- Concatenation using
concat()andappend()to combine multiple frames - Loops to iterate data frames and apply functions
- GroupBy to segment and operate on groups of data frames
- Merging and Joining to link data across multiple frames
- Aggregations to calculate combined statistics
- Statistical Tests like t-tests applied after checking data compatibility
Mastering these multi-data frame operations enables efficient wrangling of complex real-world data and unified analysis across disparate frames using Python’s versatile data analysis libraries.
The key takeaways are understanding the various methods to join, apply operations, and analyze at scale across collections of interrelated data frames. This unlocks the full potential of Python for data preparation, exploration and analytics.