kopia lustrzana https://github.com/animator/learn-python
64 wiersze
2.0 KiB
Markdown
64 wiersze
2.0 KiB
Markdown
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# Pandas DataFrame
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The Pandas DataFrame is a two-dimensional, size-mutable, and possibly heterogeneous tabular data format with labelled axes. A data frame is a two-dimensional data structure in which the data can be organised in rows and columns. Pandas DataFrames are comprised of three main components: data, rows, and columns.
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In the real world, Pandas DataFrames are formed by importing datasets from existing storage, which can be a Excel file, a SQL database or CSV file. Pandas DataFrames may be constructed from lists, dictionaries, or lists of dictionaries, etc.
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Features of Pandas `DataFrame`:
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- **Size mutable**: DataFrames are mutable in size, meaning that new rows and columns can be added or removed as needed.
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- **Labeled axes**: DataFrames have labeled axes, which makes it easy to keep track of the data.
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- **Arithmetic operations**: DataFrames support arithmetic operations on rows and columns.
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- **High performance**: DataFrames are highly performant, making them ideal for working with large datasets.
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### Installation of libraries
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`pip install pandas` <br/>
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`pip install xlrd`
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- **Note**: The `xlrd` library is used for Excel operations.
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Example for reading data from an Excel File:
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```python
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import pandas as pd
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l = pd.read_excel('example.xlsx')
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d = pd.DataFrame(l)
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print(d)
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```
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Output:
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```python
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Name Age
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0 John 12
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```
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Example for Inserting Data into Excel File:
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```python
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import pandas as pd
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l = pd.read_excel('file_name.xlsx')
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d = {'Name': ['Bob', 'John'], 'Age': [12, 28]}
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d = pd.DataFrame(d)
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L = pd.concat([l, d], ignore_index = True)
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L.to_excel('file_name.xlsx', index = False)
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print(L)
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```
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Output:
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```python
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Name Age
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0 Bob 12
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1 John 28
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```
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### Usage of Pandas DataFrame:
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- Can be used to store and analyze financial data, such as stock prices, trading data, and economic data.
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- Can be used to store and analyze sensor data, such as data from temperature sensors, motion sensors, and GPS sensors.
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- Can be used to store and analyze log data, such as web server logs, application logs, and system logs
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