US Long Term Mutual Fund Monthly Net New Cash Flow


Files Size Format Created Updated License Source
2 85kB csv zip 3 months ago John Snow Labs Standard License John Snow Labs Investment Company Institute (ICI)

Data Files

File Description Size Last changed Download
us-long-term-mutual-fund-monthly-net-new-cash-flow-csv 4kB csv (4kB) , json (17kB)
us-long-term-mutual-fund-monthly-net-new-cash-flow_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 11kB zip (11kB)


This is a preview version. There might be more data in the original version.

Field information

Field Name Order Type (Format) Description
Date 1 date (%Y-%m-%d) Date of data collection
Total_Long_Term_Cash_Flows 2 integer Total long term net cash flows
Total_Equity 3 integer Total equity funds
Total_Domestic_Equity 4 integer Total domestic equity
Large_Cap_Equity 5 integer Large-cap funds invest primarily in companies with large market capitalizations, which are generally more than $5 billion or in companies with both medium and large market capitalizations.
Mid_Cap_Equity 6 integer Mid-cap funds invest primarily in companies with medium market capitalizations, generally ranging from $1 billion to $7 billion or in companies with both small and medium market capitalizations.
Small_Cap_Equity 7 integer Small-cap funds invest primarily in companies with small market capitalizations of up to $2 to 2.5 billion.
Multi_Cap_Equity 8 integer Multi-cap funds invest in companies of all market capitalizations or are not limited to companies within specific market capitalizations.
Other_Equity 9 integer Other domestic equity funds seek capital appreciation by investing in companies in related fields or by employing alternative strategies, such as long/short, market neutral, leveraged, or inverse strategies.
Total_World_Equity 10 integer Total world equity funds
Developed_Markets_Equity 11 integer Developed market funds invest primarily in equity securities traded worldwide, including funds with a global, international, or regional focus. Also included in this category are funds investing in companies traded worldwide, including emerging markets, while employing a long/short, market neutral, leveraged, or inverse strategy.
Emerging_Markets_Equity 12 integer Emerging market funds invest primarily in companies based in less-developed countries of the world.
Hybrid_Funds 13 integer Hybrid funds invest in a mix of equity and debt securities.
Total_Bond 14 integer Total bond funds
Total_Taxable_Bond 15 integer Total taxable bond funds
Investment_Grade_Taxable_Bond 16 integer Investment grade bond funds seek current income by investing primarily (80 percent) in investment grade debt securities.
High_Yield_Taxable_Bond 17 integer High-yield bond funds seek current income by investing two-thirds or more of their portfolios in lower-rated corporate bonds (Baa or lower by Moody’s and BBB or lower by Standard and Poor’s rating services) and floating rate securities.
Government_Taxable_Bond 18 integer Government bond funds pursue an objective of high current income by investing in taxable bonds issued, or backed, by the U.S. government, and include mortgage-backed securities.
Multisector_Taxable_Bond 19 integer Multisector bond funds seek to provide high current income for their shareholders by investing predominantly in a combination of domestic fixed-income securities, including mortgage-backed securities and high-yield bonds, may invest up to 25 percent in bonds issued by foreign companies and governments, and funds pursing long/short, market neutral, leveraged, and inverse strategies.
Global_Taxable_Bond 20 integer Global tacable bond or world bond funds seek current income by investing in debt securities worldwide.
Municipal_Bond 21 integer Municipal bond funds invest in municipal bonds of a single state or a national mix of municipal bonds.

Import into your tool

Data-cli or just data is the program to get and post your data with the datahub.
Use data with the almost like you use git with the github. Here are installation instructions.

data get
tree JohnSnowLabs/us-long-term-mutual-fund-monthly-net-new-cash-flow
# Get a list of dataset's resources
curl -L -s | grep path

# Get resources

curl -L

curl -L

If you are using R here's how to get the data you want quickly loaded:

install.packages("jsonlite", repos="")

json_file <- ''
json_data <- fromJSON(paste(readLines(json_file), collapse=""))

# get list of all resources:

# print all tabular data(if exists any)
for(i in 1:length(json_data$resources$datahub$type)){
    path_to_file = json_data$resources$path[i]
    data <- read.csv(url(path_to_file))

Note: You might need to run the script with root permissions if you are running on Linux machine

Install the Frictionless Data data package library and the pandas itself:

pip install datapackage
pip install pandas

Now you can use the datapackage in the Pandas:

import datapackage
import pandas as pd

data_url = ''

# to load Data Package into storage
package = datapackage.Package(data_url)

# to load only tabular data
resources = package.resources
for resource in resources:
    if resource.tabular:
        data = pd.read_csv(resource.descriptor['path'])
        print (data)

For Python, first install the `datapackage` library (all the datasets on DataHub are Data Packages):

pip install datapackage

To get Data Package into your Python environment, run following code:

from datapackage import Package

package = Package('')

# print list of all resources:

# print processed tabular data (if exists any)
for resource in package.resources:
    if resource.descriptor['datahub']['type'] == 'derived/csv':

If you are using JavaScript, please, follow instructions below:

Install data.js module using npm:

  $ npm install data.js

Once the package is installed, use the following code snippet:

const {Dataset} = require('data.js')

const path = ''

// We're using self-invoking function here as we want to use async-await syntax:
;(async () => {
  const dataset = await Dataset.load(path)
  // get list of all resources:
  for (const id in dataset.resources) {
  // get all tabular data(if exists any)
  for (const id in dataset.resources) {
    if (dataset.resources[id]._descriptor.format === "csv") {
      const file = dataset.resources[id]
      // Get a raw stream
      const stream = await
      // entire file as a buffer (be careful with large files!)
      const buffer = await file.buffer
      // print data