MIX CRYPTO DATA 2k.csv

28. Write a Pandas program to count city wise number of people from a given of data set (city, name of the person). Go to the editorSample data:city Number of people0 California 41 Georgia 22 Los Angeles 4 Click me to see the sample solution

MIX CRYPTO DATA 2k.csv

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43. Write a Pandas program to get a list of a specified column of a DataFrame. Go to the editorSample data:Powered by Original DataFramecol1 col2 col30 1 4 71 2 5 82 3 6 9Col2 of the DataFrame to list:[4, 5, 6]Click me to see the sample solution

46. Write a Pandas program to check whether a given column is present in a DataFrame or not. Go to the editorSample data:Original DataFramecol1 col2 col30 1 4 71 2 5 82 3 6 123 4 9 14 7 5 11Col4 is not present in DataFrame.Col1 is present in DataFrame.Click me to see the sample solution

Select the Date column, select Home > Transform > Data Type, and then select the Date option. You can convert other numeric types, such as percentage or currency.Note If you get a notification that data type transformations already exist, you can ignore it, since you're overwriting the date transformation that Power Query thought you wanted when you loaded the data.

Information of VC affiliation (e.g., shareholder name, equity ratio, year) was gathered from public sources such as Tianyancha (Tianyancha.com), Qichacha (Qichacha.com), Bloomberg, and Crunchbase. Specifically, we searched information for domestic VCs mainly from Tianyancha and Qichacha (the two largest databases on Chinese business registration information), and gathered information for foreign VCs mainly from their official websites, Bloomberg, and Crunchbase. For those VCs that were not captured by the above sources, we checked media reports to gather information. Moreover, if information was inconsistent across sources, we followed Bertoni and Martí12 and triangulated the data with additional information available from all potential public sources (e.g., VC websites, press releases, media reports, and IPO prospectuses).

GVC: Based on prior literature13 and our interviews with industry experts, GVCs in China include two sub-types (i.e., public agency-affiliated, SOE-affiliated). Specifically, a VC is labelled as public agency- or SOE-affiliated when one of its 1st-level shareholders is classified as public agency or SOE, respectively. In the dataset, we also provided the information of equity ratio owned by each of the two sub-types separately, to facilitate studies on private-public interactions in the entrepreneurial financing context.

GVC: The criteria for GVC are distinct from other VC types because GVC is very complex in China and involves various forms of funding from public authorities who are in charge of government-guided funds, as well as SOEs including state-owned corporates, science or high-tech parks, and public accelerators. Following prior studies that classified GVC by dominant shareholders7,13,14, we classified a VC as GVC if it satisfies both of the following criteria: (1) at least one of its 1st-level shareholders is labelled as public agency or SOE, and (2) the total equity shares owned by public agency and SOE 1st-level shareholders are greater than that of any other type of 1st-level shareholders. In the dataset, we also provided the information of total equity ratio owned by state for those records. If only having the names of 1st-level shareholders but not their equity ratio data, we revised the criteria to be: (1) at least one of its 1st-level shareholders is labelled as public agency or SOE, and (2) the total number of public agency and SOE 1st-level shareholders is greater than that of any other type of 1st-level shareholders. If information of 1st-level shareholders is lacking, a VC is labelled as GVC if it is disclosed as a public agency or SOE from public sources.

IVC, BVC, FVC, UVC, or PenVC: Lastly, if a VC does not belong to GVC or CVC, we determined its affiliation by the 1st-level shareholder type with the largest total equity, instead of the type of its absolute or relative controlling shareholder, because some shareholders may belong to the same type. Noteworthy, when a VC is owned by multiple corporates but not identified as CVC, we classified it as IVC, following the prior literature on CVC2,9. Likewise, if equity ratio data of 1st-level shareholders is lacking, we used the type with the largest number of shareholders. And if information of 1st-level shareholders is totally missing, a VC is accordingly labelled as IVC, BVC, FVC, UVC, or PenVC according to its main business revealed by public sources.

GVC_equity: the total equity owned by public agency-affiliated and SOE-affiliated 1st-level shareholders in a VC. Noteworthy, missing value in this variable means that equity ratio data is not available.

To start, we relied on CVSource database to generate a list of 6,553 VCs who have invested in China between 2000 and 2016. We then used their names to search in public sources including Tianyancha and Qichacha to collect the information of their 1st-level shareholders. This step was initially done by machine and algorism, deriving 28,968 unique 1st-level shareholders; however, VC names from CVSource could be abbreviations or former names that cannot be perfectly matched with those full names in public sources, making it problematic to solely reply on algorism. To address this issue, we manually double-checked and corrected any inconsistency to ensure that each VC name from CVSource is aligned with the name we collected from public sources. We also checked any duplications in VC names because the CVSource database may record a former and a current name of a VC but treat them as two different VCs. Finally, we derived 22,757 unique 1st-level shareholders for 6,379 unique VCs.

Next, we combined our VCAC dataset (using VC affiliation information) with the CVSource dataset (using investment deal information) to further validate the VCAC dataset by comparing with prior studies that reported similar attributes of VCs in China. The following validations merged VC affiliation data from VCAC.csv and investment deal information from CVSource, based on VC-year level.

In future, we will keep maintaining the dataset. For example, we may add new features/variables to this dataset when theoretically meaningful (either emerging from our future research or suggested by dataset users from external). If so, a log file will be available for users to understand any new features.

J. Chen initiated the project design, managed the overall process, developed the coding scheme for VC classification, collected data from CVSource, wrote Stata codes to process data, and wrote and revised the manuscript. T.Y. Chen manually oversaw the coding process, organized human coders, checked and classified the affiliation of VCs, and wrote and revised the manuscript. Y.F. Song coded on Layer-II SOE nature, checked the Layer-I coded results for all types of shareholders, identified the unique corporate parents for CVCs, and revised the manuscript. B. Hao collected data from Qichacha.com, reviewed the literature, and wrote and revised the manuscript. L. Ma collected data from Tianyancha.com.

Crypto assets have been the subject of a lot of stress and confusion when it comes to taxes. Despite its ambiguous guidance, the tax agency sent thousands of letters to crypto traders and investors warning them to pay the tax that they owe or face fines and other penalties.

You should keep a record of each transaction to ensure that you have the right cost basis on file. Otherwise, you may have to short through historical data to find the fair market value for different cryptocurrencies at different times. The good news is that you can use reputable price indexes in the process.

While the cost basis for ICO investors is fairly straightforward, the cost basis for issuers is a little less certain. The IRS says that the issuance of 'utility tokens' for cash, crypto, or other property will be treated as the sale of property in which the issuer has a zero-cost basis.

Hard forks and airdrops also create an immediate tax obligation for the current tax year. In other words, you owe tax on the cost basis (or fair market value at the time of acquisition) of the new crypto in the current tax year. The only requirement is that you have technical control over the asset.

The good news is that there has been a lot of pushback among lawmakers for better solutions. After all, many crypto holders don't have an option when it comes to being on the receiving end of a hard fork or airdrop and they may not realize any cash gains if they don't sell it.

Many exchanges prepare cost basis reports where possible, but they don't know how you originally acquired cryptocurrencies that you've imported into your account. If you use multiple wallets or exchanges, you cannot rely on exchanges to accurately report your cost basis figures.

Rather than moving to Puerto Rico to avoid paying taxes on crypto (there are caveats as well), there are a few tax strategies that you can use to minimize your long-term and short-term capital gains. Here are those strategies:

Unfortunately, you have to choose one cost basis method and be consistent. But taking a look at your portfolio before filing your crypto tax report can help you choose a method that lowers your liability as much as possible.

Waiting for the last moment in April to choose a crypto tax strategy is quaint. To be effective for the tax year, you must carry out a strategy by December 31st. Assess your portfolio in December to determine the following:

The process of manually aggregating, merging, and sorting data from multiple exchanges and wallets is tedious and time-consuming. Fortunately, crypto tax software can automate the process and help you avoid making costly mistakes.

ZenLedger simplifies the cost basis by stitching together your trading history and exporting based on the accounting method you select. After importing transactions from multiple sources, the platform automatically computes your capital gains and losses and pre-fills popular IRS forms, like Form 1040 Schedule D and 8949. ZenLedger crypto tax software provides an easy-to-use solution to find a cost basis for crypto traders who use multiple exchanges and wallets. 041b061a72