| LM |
Essential Pandas Functions for Data Analysis & Plotting |
🧩 1. Essential Pandas Functions for Data Analysis & Plotting
To create statistical diagrams with Matplotlib later on, students should be familiar with the following Pandas functions and workflow steps:
| Topic |
Important Functions |
Meaning |
| Load a file |
pd.read_csv() |
Load a CSV file |
| Quick look |
df.head(), df.tail() |
Show the first/last rows |
| Information |
df.info() |
Columns, data types, missing values |
| Statistics |
df.describe() |
Basic statistical summary |
| Count values |
df['col'].value_counts() |
e.g., number of males/females |
| Categories of value_counts |
df['col'].value_counts().index |
Returns category names (e.g., ['male','female']) |
| Frequencies of value_counts |
df['col'].value_counts().values |
Returns counts (e.g., [577, 314]) |
| Select one column |
df['col'] |
Extract a single column |
| Select multiple columns |
df[['col1', 'col2']] |
Extract several columns |
| Filter rows |
df[df['Age'] > 18] |
Select rows matching a condition |
| Filter with multiple conditions |
df[(df['Sex']=='female') & (df['Survived']==1)] |
Combine logical conditions |
| Filter by multiple values |
df[df['col'].isin(list)] |
Select rows where values are in a given list |
| Sort data |
df.sort_values('col') |
Sort DataFrame by column |
| Check missing values |
df.isna().sum() |
Count missing values per column |
| Drop missing rows |
df.dropna() |
Remove rows with missing values |
| Fill missing values |
df.fillna(value) |
Replace missing values |
| Create a new column |
df['new'] = ... |
Add derived/computed values |
| Group data |
df.groupby('col') |
Group rows by a category |
| Aggregate data |
df.groupby('col').mean() |
Compute statistics per group |
| Set x-axis limits (plots) |
plt.xlim(min, max) |
Define start and end of the x-axis |
