Plotting Data from Multiple Pandas Data Frames in One Plot

Plotting two columns from multiple data frames on one graph in matplotlib

You can use the join method which is slightly more convenient to use than the merge method in this case. As you want to plot the data according to time, it makes sense to set TIME as the index from the beginning. This makes join convenient to use seeing as it merges dataframes on the index by default. The columns of the first dataframe can be renamed for consistency in the numbering.

import os
import pandas as pd  # v 1.2.3

combine = {}
for filename in os.listdir(dir):
    if filename.endswith(".txt"):
        data = pd.read_csv(filename, delim_whitespace=True, skiprows=range(0,2))
        df = pd.DataFrame({
            "Pressure": data._get_column_array(2),
            "Rate": data._get_column_array(6),
            "Prop_con": data._get_column_array(8)
            },
            index=data._get_column_array(1))
        combine[filename] = df

# Select first dataframe and then merge others to it on the index, using
# outer merge in case the time ranges are not identical in all files
dfm = list(combine.values())[0]
for idx, df_other in enumerate(list(combine.values())[1:]):
    dfm = dfm.join(df_other, how='outer', rsuffix=idx+2)

# Rename first columns for naming consistency
dfm.columns = [col+'1' if idx < df_other.columns.size else col
               for idx, col in enumerate(dfm.columns)]

# Plot all pressures
dfm[dfm.columns[['Pressure' in col for col in dfm.columns]]].plot()

plot multiple pandas dataframes in one graph python plotly

With plotly you can try the following:

import pandas as pd
import plotly.graph_objects as go
from plotly.offline import iplot

# dict for the dataframes and their names
dfs = {"df1" : df1, "df2": df2, "df3" : df3, "df4" : df4, "df5" : df5}

# plot the data
fig = go.Figure()

for i in dfs:
    fig = fig.add_trace(go.Scatter(x = dfs[i]["day-shift"],
                                   y = dfs[i]["PRO"], 
                                   name = i))
fig.show()

plotting multiple dataframes as one plot

IIUC, try pivoting your data first, so that each column represents a type and each row represents your name:

df_piv = df_all.pivot('name', 'type', 'value')

[out]

type      all    one   two
name                      
model1  100.1  110.1  80.1
model2  121.1  131.1  85.1
model3  151.1  141.1  90.1
model4    NaN  161.1  92.1
model5    NaN  171.1  94.1

Then the plotting command you were using:

df_piv.plot(kind='bar', rot=45, figsize=(20,6), grid=True, color=['r', 'g', 'y'])

[out]

Plotting multiple pandas DataFrames in one *3D* scatterplotplot

Assuming that you have two DataFrame called df1 and df2, both containing columns 'xitem', 'yitem', 'zitem', you can plot them in this way:

for curr_df, c in zip((df1, df2), ('b', 'r')):
    ax.scatter(*curr_df[['xitem', 'yitem', 'zitem']].values.T, color=c)

Here a complete example:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

sns.set(style = "darkgrid")

df1 = pd.DataFrame(
    data=np.random.random((100, 3)) + np.array([1, 1, 1]),
    columns=['xitem', 'yitem', 'zitem'],
)

df2 = pd.DataFrame(
    data=np.random.random((100, 3)),
    columns=['xitem', 'yitem', 'zitem'],
)

fig = plt.figure()
ax = fig.add_subplot(111, projection = '3d')

for curr_df, c in zip((df1, df2), ('b', 'r')):
    ax.scatter(*curr_df[['xitem', 'yitem', 'zitem']].values.T, color=c)

ax.set_xlabel("X Label")
ax.set_ylabel("Y Label")
ax.set_zlabel("Z Label")

plt.show()

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