id stringlengths 11 28 | conversations list | instruction stringlengths 0 908 |
|---|---|---|
data_325301 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \nfrom sklearn.decomposition import PCA\n\npca = PCA(n_components=2)\nY_sklearn = pca.fit(df_micro)\nprojected = pca.fit_transform(df_micro)\n\nimport matplotlib.pyplot as plt\n%matplotlib inline\nplt.rcParam... | |
data_325302 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \nfrom sklearn.cluster import KMeans\n\nkmeans = KMeans(n_clusters=10)\nres = kmeans.fit(df_micro)\nlabels = res.labels_\nres.cluster_centers_\n\nplt.scatter(projected[:, 0], projected[:, 1],\n c=l... | |
data_325303 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n Initialisation : be careful!\nThe initialisation step requires the to set the number of clusters K. To this end, one can either use a priori information and set it manually but there also exists several... | |
data_325304 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \ncluster_range = range(1,50)\ncluster_errors = []\n\nfor num_clusters in cluster_range:\n clust = KMeans(n_clusters=num_clusters, random_state=0, n_init=10)\n clust.fit(df_micro)\n cluster_errors.append(c... | |
data_325305 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n Gap Statistic\nThe Gap Statistic was developped by reasearchers from stanford, and relies on the variation of the within-cluster variation. To have more details, you\nTo compute it we will use the imple... | |
data_325306 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n What about mixture model ?\nIn the probabilistic framework of mixture models, we assume that the data are generated according to a mixture of probability density functions, with cluster-specific paramet... | |
data_325307 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n Basic statistics\nCalculate number of rows, unique institutions, etc in the payments data."
},
{
"from": "gpt",
"value": "print \"{:,} rows\".format(len(df))\nprint \"£{:,} total declared\".... | |
data_325308 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \ndf_hcps = df[pd.isnull(df['Organisation Name'])]\ndf_hcos = df[~pd.isnull(df['Organisation Name'])]\na = \"{:,} payments to HCOs ({:,.2f}% of total payments)\"\nprint a.format(len(df_hcos), 100 * len(df_hco... | |
data_325309 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n Most payments are small! 50% are below £355, 75% are below £800. Only 1,164 payments are more than £10,000."
},
{
"from": "gpt",
"value": "print df['Amount'].describe()\nprint len(df[df['Amo... | |
data_325310 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \ndf_hcps.sort_values('Amount', ascending=False).head()"
},
{
"from": "gpt",
"value": "What are the biggest single payments to individuals?\nPrint the top individual payments... they're all 'Fees ... | |
data_325311 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n Why are there a few negative payments?"
},
{
"from": "gpt",
"value": "df.sort_values('Amount').head()[['Company Name', 'Organisation Name', 'TOV Type', 'Amount']]"
}
] | |
data_325312 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n Payment distribution by each TOV category/type, in the payments data."
},
{
"from": "gpt",
"value": "df.groupby(('TOV Category', 'TOV Type')).sum().sort_values('Amount', ascending=False)"
... | |
data_325313 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n Start to compare the declared and undeclared data\nNow we can start comparing the \"payments\" (declared) data with the \"aggregate\" (undeclared) data. We know that the aggregate data sheet only contai... | |
data_325314 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n How much is missing?\nBy my calculations, the declared payments cover £81m, and the undeclared payments cover £282m, so 78% of all spending is undeclared. Note that \"R&D\" makes up a big chunk of this ... | |
data_325315 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n Work out how many HCP payments are missing, by company\nWe know that rows with a blank 'Organisation Name' column in the aggregate data relate to HCPs. And we know that rows with a blank 'Organisation N... | |
data_325316 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n Work out how many HCOs/HCPs are missing, by company\nWith the limited and redacted data we have, perhaps the most meaningful thing we can look at is the amount of data that is undeclared!\nThe only thin... | |
data_325317 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n Compare undeclared payments by category\nAs above, but looking for which categories have the largest share of undeclared payments."
},
{
"from": "gpt",
"value": "# As above, EXCLUDE R&D. \nd... | |
data_325318 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n The individuals with the most declared payments\nAnyone using this should check manually that each of these is truly the same person, rather than several people with the same name. We could further redu... | |
data_325319 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \ndf_by_co = df.groupby('Company Name').agg(['sum', 'mean', 'median', 'count'])\ndf_by_co.sort_values(by=('Amount', 'sum'), ascending=False).head(10)"
},
{
"from": "gpt",
"value": "Payments by pha... | |
data_325320 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n Payments by pharma companies by category\nBreak down the above table by category, too. Again, this is only declared payments."
},
{
"from": "gpt",
"value": "# df[['Company Name', 'TOV Categ... | |
data_325321 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n Payments by pharma companies by speciality\nBreak down the above table by speciality, too. Again, this is only declared payments."
},
{
"from": "gpt",
"value": "df_by_co_and_speciality = df.... | |
data_325322 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n Payments by organisation\nAgain, these are the declared payments - we can't break down the aggregate payments this way."
},
{
"from": "gpt",
"value": "df_by_org = df.groupby('Organisation Na... | |
data_325323 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \ndf_by_spec = df.groupby('Speciality').agg(['sum', 'mean', 'median', 'count']).sort_values(by=('Amount', 'sum'), ascending=False)\nprint df_by_spec.head(10)\ndf_by_spec.to_csv('by_speciality.csv')"
},
{
... | |
data_325324 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \n# Normalise the specialties\ndef specialty_classifier(row):\n other = ['Healthcare Administration',\n 'Microbiology',\n 'Laboratory - Medical Analysis',\n 'Research',\... | |
data_325325 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n Unique individuals by speciality\nWe can work out how many uniquely-named individuals there are in each speciality as follows.\n\"General Medicine\" probably != GPs, since Raj Acquilla is in this catego... | |
data_325326 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n The same thing, with custom specialty groupings"
},
{
"from": "gpt",
"value": "df_individual = df.groupby(('Last Name', 'First Name', 'Normalised specialty')).sum().reset_index()\ndf_individ... | |
data_325327 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n Exploratory work to understand undeclared payments\nThis isn't interesting of itself, but I'm trying to work out what the various columns in the Aggregates tab mean. \nThis just here as an example of da... | |
data_325328 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \n\n%pylab inline\nplt.figure()\nbin_range = np.arange(0, 1100000, 10000)\ndf['Amount'].plot(kind='hist', bins=bin_range, color='blue', alpha=0.6)\nplt.ylabel(\"Number of payments\")\nplt.xlabel(\"Payment siz... | |
data_325329 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \ninit = State(R=200, L=0, E=0, V=4e-7)"
},
{
"from": "gpt",
"value": "During the initial phase of HIV infection, the concentration of the virus in the bloodstream typically increases quickly and ... | |
data_325330 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \ngamma = 1.36\nmu = 1.36e-3\ntau = 0.2\nbeta = 0.00027\np = 0.1\nalpha = 3.6e-2\nsigma = 2\ndelta = 0.33\npi = 100"
},
{
"from": "gpt",
"value": "The behavior of the system is controlled by 9 par... | |
data_325331 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \nsystem = System(init=init,\n t_end=120,\n num=481)"
},
{
"from": "gpt",
"value": "Here's a System object with the initial conditions and the duration of the simulat... | |
data_325332 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \n# Solution\n\ndef slope_func(t, state, system):\n R, L, E, V = state\n\n infections = beta * R * V\n conversions = alpha * L\n \n dRdt = gamma * tau - mu * R - infections\n dLdt = p * infe... | |
data_325333 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n Test your slope function with the initial conditions.\nThe results should be approximately\n-2.16e-08, 2.16e-09, 1.944e-08, -8e-07"
},
{
"from": "gpt",
"value": "# Solution\n\nslope_func(0, ... | |
data_325334 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n Exercise: Now use run_solve_ivp to simulate the system of equations."
},
{
"from": "gpt",
"value": "# Solution\n\nresults, details = run_solve_ivp(system, slope_func)\ndetails.message\n\n# S... | |
data_325335 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \nresults.V.plot(label='V')\ndecorate(xlabel='Time (days)',\n ylabel='Free virions V',\n yscale='log',\n ylim=[0.1, 1e4])\n\nresults.R.plot(label='R', color='C1')\ndecorate(xlabel='Tim... | |
data_325336 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n First, you'll need a shape. You can use use GeoJSON or, in case you want to use a standard watershed, use a HUC-8, HUC-10, or HUC-12 code. The Philadelphia-Schuylkill River HUC-12 has the code 020402031... | |
data_325337 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n With this, we have enough to start modeling. The model is run in 2 steps, a Prepare step, and a Run step. Let's import some libraries that we'll use throughout:"
},
{
"from": "gpt",
"value":... | |
data_325338 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \nDATA = {'huc': HUC}\nHEADERS = {'Authorization': f'Token {MMW_API_TOKEN}'}"
},
{
"from": "gpt",
"value": "Prepare\nIn the Prepare step, we give the API a shape, and it queries a number of differ... | |
data_325339 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \nres = requests.post(f'{MMW_API_URL}/modeling/gwlf-e/prepare/', json=DATA, headers=HEADERS)\nres.json()"
},
{
"from": "gpt",
"value": "Now we have can make the request like:"
}
] | |
data_325340 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \nPREPARE_JOB_UUID = res.json()['job_uuid']\nres = requests.get(f'{MMW_API_URL}/jobs/{PREPARE_JOB_UUID}/', headers=HEADERS)\npprint.pprint(res.json(), depth=3)"
},
{
"from": "gpt",
"value": "We se... | |
data_325341 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n Run\nThe status of the result above shows that the job is complete. The output is under the result key. This is a JSON representation of a GMS file, which is used to capture the entire input required fo... | |
data_325342 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n And then we can check the result of that job to see the final values:"
},
{
"from": "gpt",
"value": "res = requests.get(f'{MMW_API_URL}/jobs/{RUN_JOB_UUID}/', headers=HEADERS)\nres.json()"
... | |
data_325343 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \nresult = res.json()['result']\n\nresult['meta']"
},
{
"from": "gpt",
"value": "By breaking this down a bit, we see that the result has a few different sections:"
}
] | |
data_325344 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n The first section, meta, holds values for the weather data: WxYrBeg and WxYrEnd specify the beginning and end of the weather data, and NYrs is the count of years. NLU is the total number of land use typ... | |
data_325345 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \nresult['monthly']"
},
{
"from": "gpt",
"value": "The monthly array contains modeled average evapotranspiration, groundwater levels, runoff, stream flow, point source flow, tile drain, and withdr... | |
data_325346 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n The above is used to generate Hydrology graphs in Model My Watershed.\nThe next is SummaryLoads, which has summarized information for:\n\nTotal Loads\nLoading Rates\nMean Annual Concentration\nMean Low-... | |
data_325347 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \nresult['Loads']"
},
{
"from": "gpt",
"value": "each of which contains values for nitrogen, phosphorous, and sediments.\nFinally, we have Loads, which contain values per source of pollutant:"
}... | |
data_325348 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n Modifications\nLet's pay attention to the numbers for Low-Density Open Space:"
},
{
"from": "gpt",
"value": "[l for l in result['Loads'] if l['Source'] == 'Low-Density Open Space'][0]"
}
] | |
data_325349 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n Now let's say we want to modify the input scenario, but moving all the Open Land and Barren Areas to Low-Density Mixed. We can run GWLF-E with an additional modifications array. Each item in the array i... | |
data_325350 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \nres = requests.post(\n f'{MMW_API_URL}/modeling/gwlf-e/run/',\n json={'job_uuid': PREPARE_JOB_UUID, 'modifications': MODIFICATIONS},\n headers=HEADERS)\nRUN_JOB_UUID = res.json()['job_uuid']\n\nres... | |
data_325351 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \np = 12 \nn_m = 600 # [r/min]"
},
{
"from": "gpt",
"value": "Description\nHow many pulses per second must be supplied to the control unit of the motor in Problem 9-8 to achieve\na rotational s... | |
data_325352 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n SOLUTION\nFrom Equation (9-20),\n$$n_m = \\frac{1}{3p}n_\\text{pulses}$$"
},
{
"from": "gpt",
"value": "n_pulses = 3*p*n_m\nprint('''\nn_pulses = {:.0f} pulses/min = {:.0f} pulses/sec\n=====... | |
data_325353 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n Call the brownian function to simulate a Wiener process with 1000 steps and max time of 1.0. Save the results as two arrays t and W."
},
{
"from": "gpt",
"value": "# YOUR CODE HERE\n# t=brow... | |
data_325354 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n Visualize the process using plt.plot with t on the x-axis and W(t) on the y-axis. Label your x and y axes."
},
{
"from": "gpt",
"value": "# YOUR CODE HERE\nplt.plot(t,W)\nplt.title (\"Browni... | |
data_325355 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n Use np.diff to compute the changes at each step of the motion, dW, and then compute the mean and standard deviation of those differences."
},
{
"from": "gpt",
"value": "# YOUR CODE HERE\ndW=... | |
data_325356 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n Write a function that takes $W(t)$ and converts it to geometric Brownian motion using the equation:\n$$\nX(t) = X_0 e^{((\\mu - \\sigma^2/2)t + \\sigma W(t))}\n$$\nUse Numpy ufuncs and no loops in your ... | |
data_325357 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n Use your function to simulate geometric brownian motion, $X(t)$ for $X_0=1.0$, $\\mu=0.5$ and $\\sigma=0.3$ with the Wiener process you computed above.\nVisualize the process using plt.plot with t on th... | |
data_325358 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n We will be using Wes McKinney's GitHub notebook as a skeleton. He imports the following libraries for later use:"
},
{
"from": "gpt",
"value": "from __future__ import division\nfrom numpy.ra... | |
data_325359 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n Introduction to pandas data structures\nSeries\nConsider the following input:"
},
{
"from": "gpt",
"value": "obj = Series([4, 7, -5, 3])\nobj"
}
] | |
data_325360 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n We have set the variable obj to reference a new Pandas Series, which we initialized by giving a Python list as input. Notice that Pandas automatically interprets the input data as type int64, which indi... | |
data_325361 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n Indexing\nAs previously mentioned, the natural index simply starts at 0 and increments integers to the size of the list of the input values. Alternatively, we can specify the index explicitly when we in... | |
data_325362 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n What happens when you examine the index now?"
},
{
"from": "gpt",
"value": "obj2.index"
}
] | |
data_325363 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n Don't be alarmed by the u prefix to each of the index values. In Python (as well as in other languages, this simply indicates a Unicode string. Ostensibly, there is no difference between normal strings ... | |
data_325364 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \nobj2['d'] = 6\nobj2[['c', 'a', 'd']]"
},
{
"from": "gpt",
"value": "This allows you to change the value of specific entries in your Series data. Additionally, you can call subSeries by specifyin... | |
data_325365 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n A powerful tool in Pandas is the ability to concisely access data meeting Boolean qualifications. In the case below, obj2 > 0 is given as the \"index,\" and the output is the subSeries of obj2 for wh... | |
data_325366 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \nobj2 > 0"
},
{
"from": "gpt",
"value": "Aside\nWhat is obj2 > 0 actually?"
}
] | |
data_325367 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \narr = np.random.rand(5) * 2.0 - 1.0"
},
{
"from": "gpt",
"value": "This is actually a neat property of Pandas which is similar to NumPy. In NumPy, suppose you are given an array:"
}
] | |
data_325368 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \narr"
},
{
"from": "gpt",
"value": "The actual array itself is given by"
}
] | |
data_325369 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \nboolArr = arr > 0.0\nboolArr"
},
{
"from": "gpt",
"value": "The Boolean array specifying which elements of arr are positive is given by"
}
] | |
data_325370 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \nobj2 * 2"
},
{
"from": "gpt",
"value": "Similarly, you can generate a new Series of Boolean values by subjecting the original Series to a Boolean statement, as we did above.\nBroadcasting\nLike ... | |
data_325371 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \nnp.exp(obj2)"
},
{
"from": "gpt",
"value": "returns a Series whose values are doubled, and"
}
] | |
data_325372 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n returns a Series whose values have all been subject to the transformation $x\\mapsto e^x$. Notice additionally that the dtype of obj2 has automatically been changed from int64 to float64. Again, Pandas ... | |
data_325373 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n is equivalent to noting that if $$\\text{Odds} = { n : 0 \\leq n < 20 \\text{ and } n \\text{ is odd}}$$ we have that $$3 \\in \\text{Odds}$$ while $$2 \\notin \\text{Odds}.$$\n(In fact, we have already... | |
data_325374 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \nsdata = {'Ohio': 35000, 'Texas': 71000, 'Oregon': 16000, 'Utah': 5000}\nobj3 = Series(sdata)\nobj3"
},
{
"from": "gpt",
"value": "Aside\nWe have talked about the native Python list data type. Th... | |
data_325375 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n What happens when you use an existing dataset with a new index, in which there is a new, unfilled index?"
},
{
"from": "gpt",
"value": "states = ['California', 'Ohio', 'Oregon', 'Texas']\nob... | |
data_325376 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \npd.isnull(obj4)"
},
{
"from": "gpt",
"value": "In this case, California is a previously-unused index, which has no corresponding value. Thus, Pandas initializes the new Series with the value cor... | |
data_325377 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n The notnull method does the exact opposite!"
},
{
"from": "gpt",
"value": "pd.notnull(obj4)"
}
] | |
data_325378 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n The methods isnull and notnull are \"static\" in the sense that they can be called straight from the pd module or for a specific Series object."
},
{
"from": "gpt",
"value": "obj4.isnull()"
... | |
data_325379 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \nprint \"\\tobj3:\\n\",obj3, \"\\n\\n\\tobj4:\\n\", obj4"
},
{
"from": "gpt",
"value": "Recall the two Series, obj3 and obj4:"
}
] | |
data_325380 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \nobj3 + obj4"
},
{
"from": "gpt",
"value": "Arithmetic operations between distinct Series objects work conservatively. For data types, int64 + float64 = float64 to preserve the decimal informatio... | |
data_325381 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n No entry for California exists in obj3, while no entry for Utah exists in obj4. Pandas interprets NaN + x = NaN for all x, so the resultant Series sets NaN for both California and Utah.\nWe can set some... | |
data_325382 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n You can also completely change the index at any time. This is something we will get into more detail later."
},
{
"from": "gpt",
"value": "obj.index = ['Bob', 'Steve', 'Jeff', 'Ryan']\nobj"
... | |
data_325383 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n DataFrame\nLike we said before, you can think of a DataFrame as an array of Series objects. Specifically, a DataFrame is a two-dimensional array of Series objects, all indexed by the same index series. ... | |
data_325384 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n You can reorder the columns in a new DataFrame using the following argument:"
},
{
"from": "gpt",
"value": "DataFrame(data, columns=['year', 'state', 'pop'])"
}
] | |
data_325385 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n Similarly, the index optional argument in DataFrame allows you to specify the index list. Additionally, adding a debt column with no corresponding data in data will initialize a column filled with NaN e... | |
data_325386 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \nframe2.columns"
},
{
"from": "gpt",
"value": "You can access the columns of a DataFrame as follows:"
}
] | |
data_325387 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n You can slice a particular column by specifying its column name. Notice how this returns a Series."
},
{
"from": "gpt",
"value": "frame2['state']"
}
] | |
data_325388 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \nframe2.year"
},
{
"from": "gpt",
"value": "Alternatively, you can slice a column using the following syntax:"
}
] | |
data_325389 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \nframe2.ix['three']"
},
{
"from": "gpt",
"value": "To slice a row, you can specify an index, which will return a Series representing the row at the index."
}
] | |
data_325390 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \nframe2['debt'] = 16.5\nframe2"
},
{
"from": "gpt",
"value": "Broadcasting works in the natural way that you might expect:"
}
] | |
data_325391 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n You can also give a particular column a list or ndarray, which will then be distributed across the column."
},
{
"from": "gpt",
"value": "frame2['debt'] = np.arange(5.)\nframe2"
}
] | |
data_325392 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n Finally, you can give a column of a DataFrame a Series. If you specify a Series with an index differing from the main DataFrame, then the entries of the DataFrame will be set to NaN."
},
{
"from... | |
data_325393 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \nframe2['eastern'] = frame2.state == 'Ohio'\nframe2"
},
{
"from": "gpt",
"value": "The point of Pandas is there are numerous ways to achieve the same effect, depending on whatever is easiest for ... | |
data_325394 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n We can also use Python's del function to remove a column:"
},
{
"from": "gpt",
"value": "del frame2['eastern']\nframe2.columns"
}
] | |
data_325395 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \npop = {'Nevada': {2001: 2.4, 2002: 2.9},\n 'Ohio': {2000: 1.5, 2001: 1.7, 2002: 3.6}}\n\nframe3 = DataFrame(pop)\nframe3"
},
{
"from": "gpt",
"value": "One final way to initialize DataFram... | |
data_325396 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n You can transpose a DataFrame if it makes more sense to work with the rows and columns flipped."
},
{
"from": "gpt",
"value": "frame3.T"
}
] | |
data_325397 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \nDataFrame(pop, index=[2001, 2002, 2003])"
},
{
"from": "gpt",
"value": "You can do this transpose operation from the outset by manually specifying the index."
}
] | |
data_325398 | [
{
"from": "human",
"value": "Write a description that might be above this code in jupyter notebook: \npdata = {'Ohio': frame3['Ohio'][:-1],\n 'Nevada': frame3['Nevada'][:2]}\nDataFrame(pdata)\n\nframe3.index.name = 'year'; frame3.columns.name = 'state'\nframe3"
},
{
"from": "gpt",
"value... | |
data_325399 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n If you need to access the underlying ndarray from any DataFrame, use the DataFrame.values field."
},
{
"from": "gpt",
"value": "frame3.values\n\nframe2.values"
}
] | |
data_325400 | [
{
"from": "human",
"value": "Write the code based on description that is above it in jupyter notebook: \n Index objects\nThe Index is the \"metadata\" object for Series and DataFrame objects. We've seen ways of initializing Index objects before, so we will go over some features of these objects."
},
{
... |
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