A Beginner-Friendly Pandas Tutorial designed to walk you through the essentials of data manipulation and analysis using Python's powerful library, Pandas. Perfect for newcomers and those looking to refresh their skills!
Begin your data journey with Pandas and discover how to turn raw data into actionable insights.
- Creating Data Structures:
pd.DataFrame(),pd.Series() - Viewing Data:
df.head(),df.tail(),df.shape,df.dtypes,df.sample()
- Summarizing Data:
df.sum(),df.mean(),df.median(),df.max(),df.min(),df.count(),df.nunique() - Exploratory Data Analysis:
df.info(),df.describe(),df.value_counts()
- Accessing Data:
df['column_name'],df.column_name,df[['column_name']],df.loc[row_label],df.iloc[row_index] - Conditional Selection:
df[df['column'] > value],df[mask] - Advanced Indexing:
df.ix[],df.where(),df.query(),df.isin()
- Modifying DataFrames:
df.drop(),df.rename(),df.sort_values(),df.groupby(),df.get_group() - Data Type Conversion:
df['a'].astype('data_type') - Index Management:
df.set_index(),df.reset_index(),del df['col'] - Advanced Manipulation:
df.agg(),df.map(),df.rank()
- Identifying Missing Data:
df.isna(),df.isnull(),df.notnull() - Handling Missing Data:
df.dropna(),df.fillna()
- Cleaning Operations:
df.apply(),df.replace(),df.duplicated(),df.drop_duplicates()
- Combining Data:
pd.concat(),pd.merge(),pd.join()
- Date-Time Conversion:
pd.to_datetime() - Time-Series Analysis:
df.resample()
- Visual Representation:
df.plot(),df.hist(),df.boxplot()
- Reading Data:
pd.read_csv(),pd.read_excel() - Writing Data:
df.to_csv(),df.to_excel()
Dive into the world of data analysis with Pandas and start transforming data into insights today! 🌟