Data Analysis and Visualization with Python | Set 2

Prerequisites : NumPy in Python, Data Analysis Visualization with Python | Set 1

1. Storing DataFrame in CSV Format :

Pandas provide to.csv('filename', index = "False|True") function to write DataFrame into a CSV file. Here filename is the name of the CSV file that you want to create and index tells that index (if Default) of DataFrame should be overwritten or not. If we set index = False then the index is not overwritten. By Default value of index is TRUE then index is overwritten.

Example :

import pandas as pd

# assigning three series to s1, s2, s3
s1 = pd.Series([0, 4, 8])
s2 = pd.Series([1, 5, 9])
s3 = pd.Series([2, 6, 10])

# taking index and column values
dframe = pd.DataFrame([s1, s2, s3])

# assign column name
dframe.columns =['Geeks', 'For', 'Geeks']

# write data to csv file
dframe.to_csv('geeksforgeeks.csv', index = False)
dframe.to_csv('geeksforgeeks1.csv', index = True)

Output :

geeksforgeeks1.csv


geeksforgeeks2.csv

2. Handling Missing Data

The Data Analysis Phase also comprises of the ability to handle the missing data from our dataset, and not so surprisingly Pandas live up to that expectation as well. This is where dropna and/or fillna methods comes into the play. While dealing with the missing data, you as a Data Analyst are either supposed to drop the column containing the NaN values (dropna method) or fill in the missing data with mean or mode of the whole column entry (fillna method), this decision is of great significance and depends upon the data and the affect would create in our results.

• Drop the missing Data :
  Consider this is the DataFrame generated by below code :
  

  import pandas as pd
  
  # Create a DataFrame
  dframe = pd.DataFrame({'Geeks': [23, 24, 22],
                         'For': [10, 12, np.nan],
                         'geeks': [0, np.nan, np.nan]},
                         columns =['Geeks', 'For', 'geeks'])
  
  # This will remove all the
  # rows with NAN values
  
  # If axis is not defined then
  # it is along rows i.e. axis = 0
  dframe.dropna(inplace = True)
  print(dframe)
  
  # if axis is equal to 1
  dframe.dropna(axis = 1, inplace = True)
  
  print(dframe)

  Output :

  axis=0
   
  
  axis=1
  

• Fill the missing values :
  Now, to replace any NaN value with mean or mode of the data, fillna is used, which could replace all the NaN values from a particular column or even in whole DataFrame as per the requirement.

  import numpy as np
  import pandas as pd
  
  # Create a DataFrame
  dframe = pd.DataFrame({'Geeks': [23, 24, 22],
                          'For': [10, 12, np.nan],
                          'geeks': [0, np.nan, np.nan]},
                          columns = ['Geeks', 'For', 'geeks'])
  
  # Use fillna of complete Dataframe
  
  # value function will be applied on every column
  dframe.fillna(value = dframe.mean(), inplace = True)
  print(dframe)
  
  # filling value of one column
  dframe['For'].fillna(value = dframe['For'].mean(),
                                      inplace = True)
  print(dframe)

  Output :

3. Groupby Method (Aggregation) :

The groupby method allows us to group together the data based off any row or column, thus we can further apply the aggregate functions to analyze our data. Group series using mapper (dict or key function, apply given function to group, return result as series) or by a series of columns.

Consider this is the DataFrame generated by below code :

import pandas as pd
import numpy as np

# create DataFrame
dframe = pd.DataFrame({'Geeks': [23, 24, 22, 22, 23, 24],
                        'For': [10, 12, 13, 14, 15, 16],
                        'geeks': [122, 142, 112, 122, 114, 112]},
                        columns = ['Geeks', 'For', 'geeks'])

# Apply groupby and aggregate function
# max to find max value of column

# "For" and column "geeks" for every
# different value of column "Geeks".

print(dframe.groupby(['Geeks']).max())

Output :