4. Concatenating data

Concatenation data

• Concatenation
  • Combine 2 data frames into a single data frame
• Pandas concat()

concatenated = pd.concat([weather_p1, weather_p2], ignore_index=True)

print(concatenated)

• Globbing
  • Pattern matching for file names
  • Wildcards: * and ?
    • * matches any string: Any csv file: *.csv
    • ? only allow us to match 1 character: Any single character: file_?.csv
  • Find and concatenate
  • Using loops

Combining rows of data

# Concatenate uber1, uber2, and uber3

row_concat = pd.concat([uber1,uber2,uber3]) # row-wise concatenation (default axis = 0)

print(row_concat.shape)

print(row_concat.head())

Combining columns of data

# Concatenate ebola_melt and status_country column-wise

ebola_tidy = pd.concat([ebola_melt,status_country], axis=1) #Think of column-wise concatenation of data as stitching data together from the sides

print(ebola_tidy.shape)

print(ebola_tidy.head())

Finding and concatenating data (Globbing)

import glob

import pandas as pd

pattern = '*.csv' # Write the pattern: pattern

csv_files = glob.glob(pattern) # Save all file matches

print(csv_files) # Print the file names

csv2 = pd.read_csv(csv_files[1]) # Load the second file into a DataFrame

print(csv2.head())

frames = [] # Create an empty list: frames

for csv in csv_files: # Iterate over csv files

df = pd.read_csv(csv) # Read csv into a DataFrame

frames.append(df) # Append df to frames

uber = pd.concat(frames) # Concatenate frames into a single DataFrame

print(uber.shape)

print(uber.head())