/PandasOverviewCommands

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PandasOverviewCommands

Contents

1. Installation

1.1. How install

Install Pandas: You can install Pandas using pip or conda in your command prompt or terminal:

  • pip install pandas
  • conda install pandas

1.2 How import

Import Pandas: To use Pandas in your Python script, you first need to import it:

  • import pandas as pd
  • The pd alias is commonly used to refer to Pandas.

2. Explorating data-methods-attributes

Load Data: You can load data into a Pandas DataFrame using various methods, such as read_csv(), read_excel(), read_sql(), etc. Here's an example using read_csv():

  • df = pd.read_csv('data.csv')
  • This will load the data from the data.csv file into a Pandas DataFrame called df.

Exploring Data: You can explore the data in your DataFrame using various methods and attributes. Some useful ones are:

  • df.head() # to display the first few rows of data
  • df.tail() # to display the last few rows of data
  • df.info() # to display information about the DataFrame
  • df.describe() # to display summary statistics for the DataFrame

3. Indexing and selection

Indexing and Selection: You can select data from your DataFrame using various methods, such as indexing by position, indexing by label, boolean indexing, etc. Here are some examples:

  • df.iloc[0] # select the first row of data
  • df.loc[0] # select the row with the label '0'
  • df[df['column'] > 0] # select rows where 'column' is greater than 0

4. Data cleaning

Data Cleaning: You can clean your data by handling missing values, removing duplicates, renaming columns, etc. Here are some examples:

  • df.dropna() # remove rows with missing values
  • df.drop_duplicates() # remove duplicate rows
  • df.rename(columns={'old_name': 'new_name'}) # rename columns

5. Data manipulation

Data Manipulation: You can manipulate your data by adding, deleting, or modifying columns, grouping data, sorting data, etc. Here are some examples:

  • df['new_column'] = df['column1'] + df['column2'] # add a new column
  • del df['column'] # delete a column
  • df.groupby('column').mean() # group data by 'column' and calculate the mean for each group
  • df.sort_values('column', ascending=False) # sort data by 'column' in descending order