<jupyter_start><jupyter_text>The instruction for data acquired from neuPrint The instructions below was based on the description from [neuPrintExplorer](https://neuprint.janelia.org/help/cypherexamples). For more information, please visit the original website. A technical neuPrint paper exists [here](https://www.biorxiv.org/content/10.1101/2020.01.16.909465v1.full).  Detailed information about the data<jupyter_code>import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv('Neuprint_connections.csv')
df.head
Id_pre = list(df['bodyId_pre'])
Id_post = list(df['bodyId_post'])
R_l = list(df['roi'])
wt = list(df['weight'])
t_pre = list(df['type_pre'])
ins_pre = list(df['instance_pre'])
t_post = list(df['type_post'])
ins_post = list(df['instance_post'])<jupyter_output><empty_output><jupyter_text>bodyIdbodyId is the unique identifier of a body in neuPrint. A body is a segmentation piece with at least 1 synapse. They are treated as nodes in this graph. nodes and edgesAs shown above, the total number of edges in this graph is 8034510.<jupyter_code>print('The number of source nodes is', len(pd.value_counts(Id_pre)))
print('The number of target nodes is', len(pd.value_counts(Id_post)))
print('The total number of nodes is', len(pd.value_counts(Id_pre + Id_post)))<jupyter_output>The total number of nodes is 179907<jupyter_text>ROIThere are 64 regions of interest (ROI), the name of the regions and the numbers of connections in each ROI are listed below.<jupyter_code>print(pd.value_counts(R_l))<jupyter_output>LO(R)      752331
SMP(R)     494343
SLP(R)     434722
SMP(L)     403630
AVLP(R)    386241
            ...  
CAN(R)       4147
BU(R)        3208
BU(L)        2603
AB(L)        1272
PRW           478
Length: 64, dtype: int64<jupyter_text>weightWeight indicates the number of synapese in a connection between two neurons. - It ranges from 1 to 1409 in this graph. The total counts for the connections with different weight are listed below. - The first column shows the value of the weight, and the sencond column shows the number of connections with corresponding weight value.<jupyter_code>print(pd.value_counts(wt))<jupyter_output>1       4663656
2       1449485
3        627064
4        336346
5        206011
         ...   
329           1
328           1
326           1
325           1
1409          1
Length: 398, dtype: int64<jupyter_text>typeType contains the neuron type of the body.- By default cells get a type of the form NPXXX, where NP is a acronym for the neuropil with the largest overlap, such as CL for clamp, and XXX is numeric id- If a cell is clearly recognised as a previously published type, then the systematic name will instead be replaced by that published name- The types and the counts are listed in the blocks below. The first block shows the types of presynaptic neurons, and the second block shows the types of postsynaptic neurons. The first column in each blocks shows the name of the cell types, and the sencond column shows the number of of the corresponding type.<jupyter_code>print(pd.value_counts(t_pre))
print(pd.value_counts(t_post))<jupyter_output>KCg-m      184800
KCab-m      98311
KCab-s      61634
KCab-c      56578
LC10        43556
            ...  
PS229          34
H1             26
DNp28          19
ORN_VM2        15
OCG09          12
Length: 5620, dtype: int64<jupyter_text>instanceInstance indicates a name that indicates a more specific instance of a neuron type.- The instance and the counts are listed in the blocks below. The first block shows the instance of presynaptic neurons, and the second block shows the instance of postsynaptic neurons. The first column in each blocks shows the name of the instance, and the sencond column shows the number of of the corresponding instance.<jupyter_code>print(pd.value_counts(ins_pre))
print(pd.value_counts(ins_post))<jupyter_output>KCg-m_R      184800
KCab-m_R      98311
KCab-s_R      61634
KCab-c_R      56578
LC10          42490
              ...  
(PVM02)_R         5
Franken1          5
Franken6          5
(PDM31)_R         4
(AVL22)_R         2
Length: 7802, dtype: int64
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