import pandas as pd
dir(pd.DataFrame)

Output:

 [
.....
'pipe',
 'pivot',
 'pivot_table',
 'plot',
 'pop'
...
]

Progarm

print(pd.DataFrame.plot.__doc__)

Output

Make plots of Series or DataFrame.

    Uses the backend specified by the
    option ``plotting.backend``. By default, matplotlib is used.

    Parameters
    ----------
    data : Series or DataFrame
        The object for which the method is called.
    x : label or position, default None
        Only used if data is a DataFrame.
    y : label, position or list of label, positions, default None
        Allows plotting of one column versus another. Only used if data is a
        DataFrame.
    kind : str
        The kind of plot to produce:

        - 'line' : line plot (default)
        - 'bar' : vertical bar plot
        - 'barh' : horizontal bar plot
        - 'hist' : histogram
        - 'box' : boxplot
        - 'kde' : Kernel Density Estimation plot
        - 'density' : same as 'kde'
        - 'area' : area plot
        - 'pie' : pie plot
        - 'scatter' : scatter plot (DataFrame only)
        - 'hexbin' : hexbin plot (DataFrame only)

Program

dict = {"country": ["Brazil", "Russia", "India", "China", "South Africa"],
       "capital": ["Brasilia", "Moscow", "New Dehli", "Beijing", "Pretoria"],
       "area": [8.516, 17.10, 3.286, 9.597, 1.221],
        "count01": [8.516, 17.10, 3.286, 9.597, 1.221],
       "population": [200.4, 143.5, 1252, 1357, 52.98] }
brics = pd.DataFrame(dict)

print(brics)

Output

        country    capital    area  count01  population
0        Brazil   Brasilia   8.516    8.516      200.40
1        Russia     Moscow  17.100   17.100      143.50
2         India  New Dehli   3.286    3.286     1252.00
3         China    Beijing   9.597    9.597     1357.00
4  South Africa   Pretoria   1.221    1.221       52.98

Program

# ---------------------------
# 1. BAR PLOT (Area by Country)
# ---------------------------
brics.plot(kind='bar', x='country', y='area', title='Area of BRICS Countries')
plt.ylabel("Area (million sq km)")
plt.show()

# -----------------------------------
# 2. HISTOGRAM (Population distribution)
# -----------------------------------
brics['population'].plot(kind='hist', title='Population Histogram')
plt.xlabel("Population (millions)")
plt.show()

# ---------------------------
# 3. LINE PLOT (Area trend)
# ---------------------------
brics.plot(kind='line', x='country', y='area', title='Area Line Plot')
plt.ylabel("Area (million sq km)")
plt.show()

# -----------------------------------------
# 4. SCATTER PLOT (Area vs Population)
# -----------------------------------------
brics.plot(kind='scatter', x='area', y='population',
           title='Scatter Plot: Area vs Population')
plt.xlabel("Area (million sq km)")
plt.ylabel("Population (millions)")
plt.show()

Output