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pandas Eğitim Dökümanı

Pandas Eğitim Dökümanı

• DERS - 01: - Importing libraries - Creating data sets - Creating data frames - Reading from CSV - Exporting to CSV - Finding maximums - Plotting data
• DERS - 02: - Reading from TXT - Exporting to TXT - Selecting top/bottom records - Descriptive statistics - Grouping/sorting data
• DERS - 03: - Creating functions - Reading from EXCEL - Exporting to EXCEL - Outliers - Lambda functions - Slice and dice data
• DERS - 04: - Adding/deleting columns - Index operations
• DERS - 05: - Stack/Unstack/Transpose functions
• DERS - 06 - GroupBy function
• DERS - 07 - Ways to calculate outliers
• DERS - 08: - Read from Microsoft SQL databases
• DERS - 09: - Export to CSV/EXCEL/TXT
• DERS - 10: - Converting between different kinds of formats
• DERS - 11: - Combining data from various sources

Pandas Kütüphanesi Ders - 11

Lesson 11

These tutorials are also available through an email course, please visit http://www.hedaro.com/pandas-tutorial to sign up today.

Grab data from multiple excel files and merge them into a single dataframe.

In [1]:

import pandas as pd
import matplotlib
import os
import sys
%matplotlib inline

In [2]:

print('Python version ' + sys.version)
print('Pandas version ' + pd.__version__)
print('Matplotlib version ' + matplotlib.__version__)

Python version 3.5.1 |Anaconda custom (64-bit)| (default, Feb 16 2016, 09:49:46) [MSC v.1900 64 bit (AMD64)]
Pandas version 0.20.1
Matplotlib version 1.5.1

Create 3 excel files

In [3]:

# Create DataFrame
d = {'Channel':[1], 'Number':[255]}
df = pd.DataFrame(d)
df

Out[3]:

	Channel	Number
0	1	255

In [4]:

# Export to Excel

df.to_excel('test1.xlsx', sheet_name = 'test1', index = False)
df.to_excel('test2.xlsx', sheet_name = 'test2', index = False)
df.to_excel('test3.xlsx', sheet_name = 'test3', index = False)
print('Done')

Done

Place all three Excel files into a DataFrame

Get a list of file names but make sure there are no other excel files present in the folder.

In [5]:

# List to hold file names
FileNames = []

# Your path will be different, please modify the path below.
os.chdir(r"C:\Users\david\notebooks\update")

# Find any file that ends with ".xlsx"
for files in os.listdir("."):
    if files.endswith(".xlsx"):
        FileNames.append(files)
        
FileNames

Out[5]:

['test1.xlsx', 'test2.xlsx', 'test3.xlsx']

Create a function to process all of the excel files.

In [6]:

def GetFile(fnombre):

    # Path to excel file
    # Your path will be different, please modify the path below.
    location = r'C:\Users\david\notebooks\update\\' + fnombre
    
    # Parse the excel file
    # 0 = first sheet
    df = pd.read_excel(location, 0)
    
    # Tag record to file name
    df['File'] = fnombre
    
    # Make the "File" column the index of the df
    return df.set_index(['File'])

Go through each file name, create a dataframe, and add it to a list.

i.e.
df_list = [df, df, df]

In [7]:

# Create a list of dataframes
df_list = [GetFile(fname) for fname in FileNames]
df_list

Out[7]:

[            Channel  Number
 File                       
 test1.xlsx        1     255,             Channel  Number
 File                       
 test2.xlsx        1     255,             Channel  Number
 File                       
 test3.xlsx        1     255]

In [8]:

# Combine all of the dataframes into one
big_df = pd.concat(df_list)
big_df

Out[8]:

	Channel	Number
File
test1.xlsx	1	255
test2.xlsx	1	255
test3.xlsx	1	255

In [9]:

big_df.dtypes

Out[9]:

Channel    int64
Number     int64
dtype: object

In [10]:

# Plot it!
big_df['Channel'].plot.bar();

This tutorial was created by HEDARO

Pandas Kütüphanesi Ders - 10

Lesson 10

These tutorials are also available through an email course, please visit http://www.hedaro.com/pandas-tutorial to sign up today.

• From DataFrame to Excel
• From Excel to DataFrame
• From DataFrame to JSON
• From JSON to DataFrame

In [1]:

import pandas as pd
import sys

In [2]:

print('Python version ' + sys.version)
print('Pandas version ' + pd.__version__)

Python version 3.5.1 |Anaconda custom (64-bit)| (default, Feb 16 2016, 09:49:46) [MSC v.1900 64 bit (AMD64)]
Pandas version 0.20.1

From DataFrame to Excel

In [3]:

# Create DataFrame
d = [1,2,3,4,5,6,7,8,9]
df = pd.DataFrame(d, columns = ['Number'])
df

Out[3]:

	Number
0	1
1	2
2	3
3	4
4	5
5	6
6	7
7	8
8	9

In [4]:

# Export to Excel
df.to_excel('Lesson10.xlsx', sheet_name = 'testing', index = False)
print('Done')

Done

From Excel to DataFrame

In [5]:

# Path to excel file
# Your path will be different, please modify the path below.
location = r'C:\Users\david\notebooks\update\Lesson10.xlsx'

# Parse the excel file
df = pd.read_excel(location, 0)
df.head()

Out[5]:

	Number
0	1
1	2
2	3
3	4
4	5

In [6]:

df.dtypes

Out[6]:

Number    int64
dtype: object

In [7]:

df.tail()

Out[7]:

	Number
4	5
5	6
6	7
7	8
8	9

From DataFrame to JSON

In [8]:

df.to_json('Lesson10.json')
print('Done')

Done

From JSON to DataFrame

In [9]:

# Your path will be different, please modify the path below.
jsonloc = r'C:\Users\david\notebooks\update\Lesson10.json'

# read json file
df2 = pd.read_json(jsonloc)

In [10]:

df2

Out[10]:

	Number
0	1
1	2
2	3
3	4
4	5
5	6
6	7
7	8
8	9

In [11]:

df2.dtypes

Out[11]:

Number    int64
dtype: object

This tutorial was created by HEDARO