Build networkx directed graph or flow chart from more than one column of pandas dataframe

I have pandas dataframe which consist of 10 columns.

each row consist a step performed by a user to online. there are total of 10 columns so all 10 step process

lets say first activity is booking a flight ticket so steps are
login website-->give src dest time-->select seats-->pay--review

enter image description here

so there are various permutations can happen at every step, I want to draw a directed graph out of all dataset.

currently networkx supports only 2 columns in

# libraries

import pandas as pd

import numpy as np

import networkx as nx

import matplotlib.pyplot as plt



# Build your graph

G=nx.from_pandas_dataframe(df, 'src', 'dest',create_using=nx.DiGraph())



# Plot it

nx.draw(G, with_labels=True)

plt.show()

can someone tell me how to d it for more than two column directed graph

asked Nov 20 '18 at 9:08

Puneet Sinha

8316

add a comment |

I have pandas dataframe which consist of 10 columns.

each row consist a step performed by a user to online. there are total of 10 columns so all 10 step process

lets say first activity is booking a flight ticket so steps are
login website-->give src dest time-->select seats-->pay--review

enter image description here

so there are various permutations can happen at every step, I want to draw a directed graph out of all dataset.

currently networkx supports only 2 columns in

# libraries

import pandas as pd

import numpy as np

import networkx as nx

import matplotlib.pyplot as plt



# Build your graph

G=nx.from_pandas_dataframe(df, 'src', 'dest',create_using=nx.DiGraph())



# Plot it

nx.draw(G, with_labels=True)

plt.show()

can someone tell me how to d it for more than two column directed graph

asked Nov 20 '18 at 9:08

Puneet Sinha

8316

add a comment |

I have pandas dataframe which consist of 10 columns.

each row consist a step performed by a user to online. there are total of 10 columns so all 10 step process

lets say first activity is booking a flight ticket so steps are
login website-->give src dest time-->select seats-->pay--review

enter image description here

so there are various permutations can happen at every step, I want to draw a directed graph out of all dataset.

currently networkx supports only 2 columns in

# libraries

import pandas as pd

import numpy as np

import networkx as nx

import matplotlib.pyplot as plt



# Build your graph

G=nx.from_pandas_dataframe(df, 'src', 'dest',create_using=nx.DiGraph())



# Plot it

nx.draw(G, with_labels=True)

plt.show()

can someone tell me how to d it for more than two column directed graph

asked Nov 20 '18 at 9:08

Puneet Sinha

8316

I have pandas dataframe which consist of 10 columns.

each row consist a step performed by a user to online. there are total of 10 columns so all 10 step process

lets say first activity is booking a flight ticket so steps are
login website-->give src dest time-->select seats-->pay--review

enter image description here

so there are various permutations can happen at every step, I want to draw a directed graph out of all dataset.

currently networkx supports only 2 columns in

# libraries

import pandas as pd

import numpy as np

import networkx as nx

import matplotlib.pyplot as plt



# Build your graph

G=nx.from_pandas_dataframe(df, 'src', 'dest',create_using=nx.DiGraph())



# Plot it

nx.draw(G, with_labels=True)

plt.show()

can someone tell me how to d it for more than two column directed graph

python pandas dataframe networkx directed-graph

asked Nov 20 '18 at 9:08

Puneet Sinha

8316

asked Nov 20 '18 at 9:08

Puneet Sinha

8316

asked Nov 20 '18 at 9:08

Puneet Sinha

8316

asked Nov 20 '18 at 9:08

Puneet Sinha

8316

asked Nov 20 '18 at 9:08

Puneet Sinha

8316

add a comment |

1 Answer
1

active

oldest

votes

networkx from_pandas_dataframe uses add_edges_from, you can do a similar thing:

# libraries

import pandas as pd

import numpy as np

import networkx as nx

import matplotlib.pyplot as plt



# Build your graph



df = pd.DataFrame(np.random.randn(2,4),columns=list('ABCD')) #Create a 4 column data frame



columns = list(df.columns.values)# Get columns name



g = nx.empty_graph(0, nx.DiGraph()) #initialize an empty graph



for i in range(len(columns)-1):

    g.add_edges_from(zip(df[columns[i]], df[columns[i+1]])) #Create edge between 2 values, between all consecutive coumns



# Plot it

nx.draw(g, with_labels=True)

plt.show()

With a result:

Resulting graph

edited Nov 20 '18 at 9:51

answered Nov 20 '18 at 9:36

Dinari

1,625422

super thanks main :)
– Puneet Sinha
Nov 22 '18 at 6:46

add a comment |

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1 Answer
1

active

oldest

votes

1 Answer
1

active

oldest

votes

networkx from_pandas_dataframe uses add_edges_from, you can do a similar thing:

# libraries

import pandas as pd

import numpy as np

import networkx as nx

import matplotlib.pyplot as plt



# Build your graph



df = pd.DataFrame(np.random.randn(2,4),columns=list('ABCD')) #Create a 4 column data frame



columns = list(df.columns.values)# Get columns name



g = nx.empty_graph(0, nx.DiGraph()) #initialize an empty graph



for i in range(len(columns)-1):

    g.add_edges_from(zip(df[columns[i]], df[columns[i+1]])) #Create edge between 2 values, between all consecutive coumns



# Plot it

nx.draw(g, with_labels=True)

plt.show()

With a result:

Resulting graph

edited Nov 20 '18 at 9:51

answered Nov 20 '18 at 9:36

Dinari

1,625422

super thanks main :)
– Puneet Sinha
Nov 22 '18 at 6:46

add a comment |

networkx from_pandas_dataframe uses add_edges_from, you can do a similar thing:

# libraries

import pandas as pd

import numpy as np

import networkx as nx

import matplotlib.pyplot as plt



# Build your graph



df = pd.DataFrame(np.random.randn(2,4),columns=list('ABCD')) #Create a 4 column data frame



columns = list(df.columns.values)# Get columns name



g = nx.empty_graph(0, nx.DiGraph()) #initialize an empty graph



for i in range(len(columns)-1):

    g.add_edges_from(zip(df[columns[i]], df[columns[i+1]])) #Create edge between 2 values, between all consecutive coumns



# Plot it

nx.draw(g, with_labels=True)

plt.show()

With a result:

Resulting graph

edited Nov 20 '18 at 9:51

answered Nov 20 '18 at 9:36

Dinari

1,625422

super thanks main :)
– Puneet Sinha
Nov 22 '18 at 6:46

add a comment |

networkx from_pandas_dataframe uses add_edges_from, you can do a similar thing:

# libraries

import pandas as pd

import numpy as np

import networkx as nx

import matplotlib.pyplot as plt



# Build your graph



df = pd.DataFrame(np.random.randn(2,4),columns=list('ABCD')) #Create a 4 column data frame



columns = list(df.columns.values)# Get columns name



g = nx.empty_graph(0, nx.DiGraph()) #initialize an empty graph



for i in range(len(columns)-1):

    g.add_edges_from(zip(df[columns[i]], df[columns[i+1]])) #Create edge between 2 values, between all consecutive coumns



# Plot it

nx.draw(g, with_labels=True)

plt.show()

With a result:

Resulting graph

edited Nov 20 '18 at 9:51

answered Nov 20 '18 at 9:36

Dinari

1,625422

networkx from_pandas_dataframe uses add_edges_from, you can do a similar thing:

# libraries

import pandas as pd

import numpy as np

import networkx as nx

import matplotlib.pyplot as plt



# Build your graph



df = pd.DataFrame(np.random.randn(2,4),columns=list('ABCD')) #Create a 4 column data frame



columns = list(df.columns.values)# Get columns name



g = nx.empty_graph(0, nx.DiGraph()) #initialize an empty graph



for i in range(len(columns)-1):

    g.add_edges_from(zip(df[columns[i]], df[columns[i+1]])) #Create edge between 2 values, between all consecutive coumns



# Plot it

nx.draw(g, with_labels=True)

plt.show()

With a result:

Resulting graph

edited Nov 20 '18 at 9:51

answered Nov 20 '18 at 9:36

Dinari

1,625422

edited Nov 20 '18 at 9:51

answered Nov 20 '18 at 9:36

Dinari

1,625422

answered Nov 20 '18 at 9:36

Dinari

1,625422

answered Nov 20 '18 at 9:36

Dinari

1,625422

super thanks main :)
– Puneet Sinha
Nov 22 '18 at 6:46

add a comment |

super thanks main :)
– Puneet Sinha
Nov 22 '18 at 6:46

super thanks main :)
– Puneet Sinha
Nov 22 '18 at 6:46

add a comment |

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