Pandas Dataframe plot rows as x values and column header as y values

Question

I have a Pandas DataFrame with displacements for different times (rows) and specific vertical locations (columns names). The goal is to plot the displacements (x axis) for the vertical location (y axis) for a given time (series).

According to the next example (time = 0, 1, 2, 3, 4 and vertical locations = 0.5, 1.5, 2.5, 3.5), how can the displacements be plotted for the times 0 and 3?

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(88)
df = pd.DataFrame({
    'time': np.arange(0, 5, 1),
    '0.5': np.random.uniform(-1, 1, size = 5),
    '1.5': np.random.uniform(-2, 2, size = 5),
    '2.5': np.random.uniform(-3, 3, size = 5),
    '3.5': np.random.uniform(-4, 4, size = 5),
    })
df = df.set_index('time')

Answer 1

You can filter your dataframe to only contain the desired rows. Either by using the positional index

filtered = df.iloc[[0,3],:]

or by using the actualy index of the dataframe,

filtered = df.iloc[(df.index == 3) | (df.index == 0),:]

You can then plot a scatter plot like this:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(88)
df = pd.DataFrame({
    'time': np.arange(0, 5, 1),
    '0.5': np.random.uniform(-1, 1, size = 5),
    '1.5': np.random.uniform(-2, 2, size = 5),
    '2.5': np.random.uniform(-3, 3, size = 5),
    '3.5': np.random.uniform(-4, 4, size = 5),
    })
df = df.set_index('time')


filtered_df = df.iloc[[0,3],:]
#filtered_df = df.iloc[(df.index == 3) | (df.index == 0),:]

loc = list(map(float, df.columns))

fig, ax = plt.subplots()
for row in filtered_df.iterrows():
    ax.scatter(row[1], loc, label=row[1].name)

plt.legend()    
plt.show()