{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Juypter in pandas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Uvažanje knjižnic\n", "Najprej moramo uvoziti knjižnico pandas. Ker bomo v zvezku tudi risali, uvozimo še Matplotlib." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import pandas as pd \n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Branje podatkov\n", "Za branje celotne datoteke v `dataframe` oziroma tabelo lahko uporabimo funkcijo `read_csv`" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "ename": "UnicodeDecodeError", "evalue": "'utf-8' codec can't decode byte 0xe8 in position 6: invalid continuation byte", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mUnicodeDecodeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'place.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[0;32m 700\u001b[0m skip_blank_lines=skip_blank_lines)\n\u001b[0;32m 701\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 702\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 703\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 704\u001b[0m \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m 427\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 428\u001b[0m \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 429\u001b[1;33m \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 430\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 431\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m 893\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m 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continuation byte" ] } ], "source": [ "df = pd.read_csv('place.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nastaviti moramo pravilno kodiranje. Z `utf8` očitno ne gre. Kaj pa `cp1250`?" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv('place.csv', encoding=\"cp1250\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Tole je šlo čez brez napake. Poglejmo si kako tabela izgleda. Prvih 5 vrstic lahko dobimo z metodo `head`:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Povprečne mesečne bruto in neto plače pri pravnih osebah javnega in zasebnega sektorja, Slovenija, mesečno
0MESEC\\t\"SEKTOR\"\\t\"Bruto plača Plača za mesec[E...
12014M01\\t\"Javni sektor\"\\t1758.50\\t1151.30
22014M01\\t\"Zasebni sektor\"\\t1421.34\\t932.14
32014M02\\t\"Javni sektor\"\\t1745.63\\t1136.41
42014M02\\t\"Zasebni sektor\"\\t1406.47\\t922.19
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" ], "text/plain": [ " Povprečne mesečne bruto in neto plače pri pravnih osebah javnega in zasebnega sektorja, Slovenija, mesečno\n", "0 MESEC\\t\"SEKTOR\"\\t\"Bruto plača Plača za mesec[E... \n", "1 2014M01\\t\"Javni sektor\"\\t1758.50\\t1151.30 \n", "2 2014M01\\t\"Zasebni sektor\"\\t1421.34\\t932.14 \n", "3 2014M02\\t\"Javni sektor\"\\t1745.63\\t1136.41 \n", "4 2014M02\\t\"Zasebni sektor\"\\t1406.47\\t922.19 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ne izgleda ravno kot tabela. Prvi dve vrstici bomo raje izpustili, glavo tabele, ki je v vrstici 3, pa bi vseeno obdržali. Število vrstic, ki jih želimo izpustiti lahko podamo preko opcijskega argumenta `skiprows`:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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MESEC\t\"SEKTOR\"\t\"Bruto plača Plača za mesec[EUR]\"\t\"Neto plača Plača za mesec[EUR]\"
02014M01\\t\"Javni sektor\"\\t1758.50\\t1151.30
12014M01\\t\"Zasebni sektor\"\\t1421.34\\t932.14
22014M02\\t\"Javni sektor\"\\t1745.63\\t1136.41
32014M02\\t\"Zasebni sektor\"\\t1406.47\\t922.19
42014M03\\t\"Javni sektor\"\\t1741.44\\t1133.47
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" ], "text/plain": [ " MESEC\\t\"SEKTOR\"\\t\"Bruto plača Plača za mesec[EUR]\"\\t\"Neto plača Plača za mesec[EUR]\"\n", "0 2014M01\\t\"Javni sektor\"\\t1758.50\\t1151.30 \n", "1 2014M01\\t\"Zasebni sektor\"\\t1421.34\\t932.14 \n", "2 2014M02\\t\"Javni sektor\"\\t1745.63\\t1136.41 \n", "3 2014M02\\t\"Zasebni sektor\"\\t1406.47\\t922.19 \n", "4 2014M03\\t\"Javni sektor\"\\t1741.44\\t1133.47 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('place.csv', encoding='cp1250', skiprows=2)\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vrstice moramo razbiti na stolpce preko tabulatorja (`\\t`). Očitno funkcija datoteko razbija po napačnem ločilu. Ločilo lahko podamo preko opcijskega argumenta `sep`:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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MESECSEKTORBruto plača Plača za mesec[EUR]Neto plača Plača za mesec[EUR]
02014M01Javni sektor1758.501151.30
12014M01Zasebni sektor1421.34932.14
22014M02Javni sektor1745.631136.41
32014M02Zasebni sektor1406.47922.19
42014M03Javni sektor1741.441133.47
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" ], "text/plain": [ " MESEC SEKTOR Bruto plača Plača za mesec[EUR] \\\n", "0 2014M01 Javni sektor 1758.50 \n", "1 2014M01 Zasebni sektor 1421.34 \n", "2 2014M02 Javni sektor 1745.63 \n", "3 2014M02 Zasebni sektor 1406.47 \n", "4 2014M03 Javni sektor 1741.44 \n", "\n", " Neto plača Plača za mesec[EUR] \n", "0 1151.30 \n", "1 932.14 \n", "2 1136.41 \n", "3 922.19 \n", "4 1133.47 " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('place.csv', encoding='cp1250', skiprows=2, sep=\"\\t\")\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Končno... Več o sami strukturi tabele lahko pogledamo z metodami, kot so `columns` in `shape`:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Index(['MESEC', 'SEKTOR', 'Bruto plača Plača za mesec[EUR]',\n", " 'Neto plača Plača za mesec[EUR]'],\n", " dtype='object')\n", "(148, 4)\n" ] } ], "source": [ "print(df.columns)\n", "print(df.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vidimo, da ima tabela 148 vrstic in 4 stolpce. Stolpcem lahko damo tudi krajša imena:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "df.columns = ['mesec', 'sektor', 'bruto', 'neto']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pogledamo lahko tudi osnovno statistiko vsebine tabele preko metode `describe`" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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brutoneto
count148.000000148.000000
mean1694.4739191099.702230
std217.336086133.755268
min1393.050000915.740000
25%1471.885000963.452500
50%1743.5350001136.695000
75%1865.5225001203.942500
max2174.5700001408.770000
\n", "
" ], "text/plain": [ " bruto neto\n", "count 148.000000 148.000000\n", "mean 1694.473919 1099.702230\n", "std 217.336086 133.755268\n", "min 1393.050000 915.740000\n", "25% 1471.885000 963.452500\n", "50% 1743.535000 1136.695000\n", "75% 1865.522500 1203.942500\n", "max 2174.570000 1408.770000" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Indeksiranje tabele\n", "Indeksiranje lahko izvedemo podobno, kot pri ostalih podatkovnih tipih jezika Python, le da tokrat v osnovi indeksiramo po stolpcih. Do stolpca `bruto` bi npr. prišli takole:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "0 1758.50\n", "1 1421.34\n", "2 1745.63\n", "3 1406.47\n", "4 1741.44\n", "5 1418.26\n", "6 1771.34\n", "7 1410.87\n", "8 1777.09\n", "9 1408.40\n", "10 1772.65\n", "11 1396.00\n", "12 1766.26\n", "13 1421.83\n", "14 1767.70\n", "15 1393.05\n", "16 1766.95\n", "17 1397.16\n", "18 1789.62\n", "19 1422.81\n", "20 1825.25\n", "21 1541.33\n", "22 1800.18\n", "23 1451.55\n", "24 1794.53\n", "25 1410.31\n", "26 1746.14\n", "27 1401.50\n", "28 1774.81\n", "29 1439.17\n", " ... \n", "118 2072.77\n", "119 1647.78\n", "120 2031.75\n", "121 1589.26\n", "122 1991.14\n", "123 1586.62\n", "124 2047.39\n", "125 1616.73\n", "126 2022.80\n", "127 1596.41\n", "128 2040.59\n", "129 1584.73\n", "130 2048.48\n", "131 1566.38\n", "132 2044.13\n", "133 1597.56\n", "134 2018.64\n", "135 1592.73\n", "136 2021.58\n", "137 1570.19\n", "138 2031.57\n", "139 1610.08\n", "140 2135.61\n", "141 1790.52\n", "142 2174.57\n", "143 1710.03\n", "144 2096.96\n", "145 1673.40\n", "146 2055.48\n", "147 1682.86\n", "Name: bruto, Length: 148, dtype: float64" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['bruto']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lahko v seznamu podamo tudi več imen stolpcev naenkrat:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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brutoneto
01758.501151.30
11421.34932.14
21745.631136.41
31406.47922.19
41741.441133.47
51418.26928.48
61771.341151.12
71410.87925.70
81777.091153.05
91408.40924.41
101772.651152.21
111396.00917.73
121766.261148.00
131421.83931.22
141767.701149.61
151393.05915.74
161766.951147.97
171397.16918.74
181789.621159.44
191422.81932.72
201825.251179.09
211541.33993.75
221800.181167.41
231451.55945.97
241794.531163.87
251410.31923.80
261746.141136.98
271401.50916.82
281774.811150.41
291439.17937.71
.........
1182072.771337.07
1191647.781082.07
1202031.751298.44
1211589.261031.63
1221991.141276.17
1231586.621030.40
1242047.391311.29
1251616.731043.97
1262022.801292.73
1271596.411033.26
1282040.591302.29
1291584.731027.42
1302048.481306.80
1311566.381016.92
1322044.131304.40
1331597.561034.57
1342018.641292.79
1351592.731032.89
1362021.581292.34
1371570.191019.47
1382031.571297.15
1391610.081043.05
1402135.611361.46
1411790.521177.51
1422174.571408.77
1431710.031126.78
1442096.961351.52
1451673.401090.55
1462055.481327.33
1471682.861098.04
\n", "

148 rows × 2 columns

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" ], "text/plain": [ " bruto neto\n", "0 1758.50 1151.30\n", "1 1421.34 932.14\n", "2 1745.63 1136.41\n", "3 1406.47 922.19\n", "4 1741.44 1133.47\n", "5 1418.26 928.48\n", "6 1771.34 1151.12\n", "7 1410.87 925.70\n", "8 1777.09 1153.05\n", "9 1408.40 924.41\n", "10 1772.65 1152.21\n", "11 1396.00 917.73\n", "12 1766.26 1148.00\n", "13 1421.83 931.22\n", "14 1767.70 1149.61\n", "15 1393.05 915.74\n", "16 1766.95 1147.97\n", "17 1397.16 918.74\n", "18 1789.62 1159.44\n", "19 1422.81 932.72\n", "20 1825.25 1179.09\n", "21 1541.33 993.75\n", "22 1800.18 1167.41\n", "23 1451.55 945.97\n", "24 1794.53 1163.87\n", "25 1410.31 923.80\n", "26 1746.14 1136.98\n", "27 1401.50 916.82\n", "28 1774.81 1150.41\n", "29 1439.17 937.71\n", ".. ... ...\n", "118 2072.77 1337.07\n", "119 1647.78 1082.07\n", "120 2031.75 1298.44\n", "121 1589.26 1031.63\n", "122 1991.14 1276.17\n", "123 1586.62 1030.40\n", "124 2047.39 1311.29\n", "125 1616.73 1043.97\n", "126 2022.80 1292.73\n", "127 1596.41 1033.26\n", "128 2040.59 1302.29\n", "129 1584.73 1027.42\n", "130 2048.48 1306.80\n", "131 1566.38 1016.92\n", "132 2044.13 1304.40\n", "133 1597.56 1034.57\n", "134 2018.64 1292.79\n", "135 1592.73 1032.89\n", "136 2021.58 1292.34\n", "137 1570.19 1019.47\n", "138 2031.57 1297.15\n", "139 1610.08 1043.05\n", "140 2135.61 1361.46\n", "141 1790.52 1177.51\n", "142 2174.57 1408.77\n", "143 1710.03 1126.78\n", "144 2096.96 1351.52\n", "145 1673.40 1090.55\n", "146 2055.48 1327.33\n", "147 1682.86 1098.04\n", "\n", "[148 rows x 2 columns]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[['bruto','neto']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Če bi želeli indeksiranje izvajati po vrsticah, lahko uporabimo metodo `loc`, ki ji kot argument podamo oznako (`index`) vrstice. " ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "mesec 2014M03\n", "sektor Zasebni sektor\n", "bruto 1418.26\n", "neto 928.48\n", "Name: 5, dtype: object" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "V osnovi je oznaka vrstice kar enaka zaporedni številki vrstice. Lahko pa jo spremenimo na kakšnega izmed obstoječih stolpcev. Uporabimo lahko metodo `set_index`, ki vrne preoblikovano tabelo s spremenjenimi oznakami vrstic. Poskusimo:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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sektorbrutoneto
mesec
2014M01Javni sektor1758.501151.30
2014M01Zasebni sektor1421.34932.14
2014M02Javni sektor1745.631136.41
2014M02Zasebni sektor1406.47922.19
2014M03Javni sektor1741.441133.47
\n", "
" ], "text/plain": [ " sektor bruto neto\n", "mesec \n", "2014M01 Javni sektor 1758.50 1151.30\n", "2014M01 Zasebni sektor 1421.34 932.14\n", "2014M02 Javni sektor 1745.63 1136.41\n", "2014M02 Zasebni sektor 1406.47 922.19\n", "2014M03 Javni sektor 1741.44 1133.47" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2 = df.set_index('mesec')\n", "df2.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Zdaj bi morali metodi `loc` podati nove oznake vrstic. Npr.:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
sektorbrutoneto
mesec
2018M02Javni sektor1888.291219.98
2018M02Zasebni sektor1518.70993.10
\n", "
" ], "text/plain": [ " sektor bruto neto\n", "mesec \n", "2018M02 Javni sektor 1888.29 1219.98\n", "2018M02 Zasebni sektor 1518.70 993.10" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2.loc['2018M02']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Skupaj z oznako vrstice pa lahko metodi `loc` podajamo tudi stolpce:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
sektorbruto
mesec
2018M02Javni sektor1888.29
2018M02Zasebni sektor1518.70
\n", "
" ], "text/plain": [ " sektor bruto\n", "mesec \n", "2018M02 Javni sektor 1888.29\n", "2018M02 Zasebni sektor 1518.70" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2.loc['2018M02', ['sektor', 'bruto']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Če želimo, da je indeksiranje neodvisno od oznak, ampak zgolj od vrstnega reda vrstic in stolpcev, uporabimo metodo `iloc`, ki ji kot argument podamo zaporedno številko vrstice, lahko pa tudi stolpca." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "mesec 2014M03\n", "sektor Zasebni sektor\n", "bruto 1418.26\n", "neto 928.48\n", "Name: 5, dtype: object" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[5]" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "928.48" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[5,3]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Z njo lahko seveda delamo tudi rezine. Če bi npr. želeli imeti vse vrstice tretjega stolpca, bi to zapisali takole:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "0 1151.30\n", "1 932.14\n", "2 1136.41\n", "3 922.19\n", "4 1133.47\n", "5 928.48\n", "6 1151.12\n", "7 925.70\n", "8 1153.05\n", "9 924.41\n", "10 1152.21\n", "11 917.73\n", "12 1148.00\n", "13 931.22\n", "14 1149.61\n", "15 915.74\n", "16 1147.97\n", "17 918.74\n", "18 1159.44\n", "19 932.72\n", "20 1179.09\n", "21 993.75\n", "22 1167.41\n", "23 945.97\n", "24 1163.87\n", "25 923.80\n", "26 1136.98\n", "27 916.82\n", "28 1150.41\n", "29 937.71\n", " ... \n", "118 1337.07\n", "119 1082.07\n", "120 1298.44\n", "121 1031.63\n", "122 1276.17\n", "123 1030.40\n", "124 1311.29\n", "125 1043.97\n", "126 1292.73\n", "127 1033.26\n", "128 1302.29\n", "129 1027.42\n", "130 1306.80\n", "131 1016.92\n", "132 1304.40\n", "133 1034.57\n", "134 1292.79\n", "135 1032.89\n", "136 1292.34\n", "137 1019.47\n", "138 1297.15\n", "139 1043.05\n", "140 1361.46\n", "141 1177.51\n", "142 1408.77\n", "143 1126.78\n", "144 1351.52\n", "145 1090.55\n", "146 1327.33\n", "147 1098.04\n", "Name: neto, Length: 148, dtype: float64" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[:,3]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Filtriranje vrednosti\n", "Vrednosti lahko filtriramo na podoben način, kot smo jih pri uporabi knjižnice `numpy`. Če nas zanima samo javni sektor, lahko stolpcev `sektor` primerjamo z vrednostjo `'Javni sektor'`." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "0 True\n", "1 False\n", "2 True\n", "3 False\n", "4 True\n", "5 False\n", "6 True\n", "7 False\n", "8 True\n", "9 False\n", "10 True\n", "11 False\n", "12 True\n", "13 False\n", "14 True\n", "15 False\n", "16 True\n", "17 False\n", "18 True\n", "19 False\n", "20 True\n", "21 False\n", "22 True\n", "23 False\n", "24 True\n", "25 False\n", "26 True\n", "27 False\n", "28 True\n", "29 False\n", " ... \n", "118 True\n", "119 False\n", "120 True\n", "121 False\n", "122 True\n", "123 False\n", "124 True\n", "125 False\n", "126 True\n", "127 False\n", "128 True\n", "129 False\n", "130 True\n", "131 False\n", "132 True\n", "133 False\n", "134 True\n", "135 False\n", "136 True\n", "137 False\n", "138 True\n", "139 False\n", "140 True\n", "141 False\n", "142 True\n", "143 False\n", "144 True\n", "145 False\n", "146 True\n", "147 False\n", "Name: sektor, Length: 148, dtype: bool" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['sektor'] == 'Javni sektor'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dobili smo tabelo vrednosti tipa `Bool`, ki jih lahko uporabimo pri indeksiranju:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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mesecsektorbrutoneto
02014M01Javni sektor1758.501151.30
22014M02Javni sektor1745.631136.41
42014M03Javni sektor1741.441133.47
62014M04Javni sektor1771.341151.12
82014M05Javni sektor1777.091153.05
102014M06Javni sektor1772.651152.21
122014M07Javni sektor1766.261148.00
142014M08Javni sektor1767.701149.61
162014M09Javni sektor1766.951147.97
182014M10Javni sektor1789.621159.44
202014M11Javni sektor1825.251179.09
222014M12Javni sektor1800.181167.41
242015M01Javni sektor1794.531163.87
262015M02Javni sektor1746.141136.98
282015M03Javni sektor1774.811150.41
302015M04Javni sektor1790.801158.63
322015M05Javni sektor1794.641160.86
342015M06Javni sektor1789.821159.24
362015M07Javni sektor1789.671158.27
382015M08Javni sektor1774.461151.70
402015M09Javni sektor1768.621148.43
422015M10Javni sektor1786.361156.42
442015M11Javni sektor1860.781200.32
462015M12Javni sektor1857.731197.92
482016M01Javni sektor1832.271183.29
502016M02Javni sektor1806.501169.82
522016M03Javni sektor1826.021180.46
542016M04Javni sektor1825.831179.61
562016M05Javni sektor1826.131178.40
582016M06Javni sektor1828.921181.19
...............
882017M09Javni sektor1895.581226.53
902017M10Javni sektor1899.841226.19
922017M11Javni sektor1957.351265.75
942017M12Javni sektor1993.111291.38
962018M01Javni sektor1936.411246.69
982018M02Javni sektor1888.291219.98
1002018M03Javni sektor1925.671243.47
1022018M04Javni sektor1939.891249.47
1042018M05Javni sektor1937.981245.43
1062018M06Javni sektor1945.781249.62
1082018M07Javni sektor1937.251246.02
1102018M08Javni sektor1931.341245.08
1122018M09Javni sektor1909.411230.80
1142018M10Javni sektor1936.001245.44
1162018M11Javni sektor1989.901283.63
1182018M12Javni sektor2072.771337.07
1202019M01Javni sektor2031.751298.44
1222019M02Javni sektor1991.141276.17
1242019M03Javni sektor2047.391311.29
1262019M04Javni sektor2022.801292.73
1282019M05Javni sektor2040.591302.29
1302019M06Javni sektor2048.481306.80
1322019M07Javni sektor2044.131304.40
1342019M08Javni sektor2018.641292.79
1362019M09Javni sektor2021.581292.34
1382019M10Javni sektor2031.571297.15
1402019M11Javni sektor2135.611361.46
1422019M12Javni sektor2174.571408.77
1442020M01Javni sektor2096.961351.52
1462020M02Javni sektor2055.481327.33
\n", "

74 rows × 4 columns

\n", "
" ], "text/plain": [ " mesec sektor bruto neto\n", "0 2014M01 Javni sektor 1758.50 1151.30\n", "2 2014M02 Javni sektor 1745.63 1136.41\n", "4 2014M03 Javni sektor 1741.44 1133.47\n", "6 2014M04 Javni sektor 1771.34 1151.12\n", "8 2014M05 Javni sektor 1777.09 1153.05\n", "10 2014M06 Javni sektor 1772.65 1152.21\n", "12 2014M07 Javni sektor 1766.26 1148.00\n", "14 2014M08 Javni sektor 1767.70 1149.61\n", "16 2014M09 Javni sektor 1766.95 1147.97\n", "18 2014M10 Javni sektor 1789.62 1159.44\n", "20 2014M11 Javni sektor 1825.25 1179.09\n", "22 2014M12 Javni sektor 1800.18 1167.41\n", "24 2015M01 Javni sektor 1794.53 1163.87\n", "26 2015M02 Javni sektor 1746.14 1136.98\n", "28 2015M03 Javni sektor 1774.81 1150.41\n", "30 2015M04 Javni sektor 1790.80 1158.63\n", "32 2015M05 Javni sektor 1794.64 1160.86\n", "34 2015M06 Javni sektor 1789.82 1159.24\n", "36 2015M07 Javni sektor 1789.67 1158.27\n", "38 2015M08 Javni sektor 1774.46 1151.70\n", "40 2015M09 Javni sektor 1768.62 1148.43\n", "42 2015M10 Javni sektor 1786.36 1156.42\n", "44 2015M11 Javni sektor 1860.78 1200.32\n", "46 2015M12 Javni sektor 1857.73 1197.92\n", "48 2016M01 Javni sektor 1832.27 1183.29\n", "50 2016M02 Javni sektor 1806.50 1169.82\n", "52 2016M03 Javni sektor 1826.02 1180.46\n", "54 2016M04 Javni sektor 1825.83 1179.61\n", "56 2016M05 Javni sektor 1826.13 1178.40\n", "58 2016M06 Javni sektor 1828.92 1181.19\n", ".. ... ... ... ...\n", "88 2017M09 Javni sektor 1895.58 1226.53\n", "90 2017M10 Javni sektor 1899.84 1226.19\n", "92 2017M11 Javni sektor 1957.35 1265.75\n", "94 2017M12 Javni sektor 1993.11 1291.38\n", "96 2018M01 Javni sektor 1936.41 1246.69\n", "98 2018M02 Javni sektor 1888.29 1219.98\n", "100 2018M03 Javni sektor 1925.67 1243.47\n", "102 2018M04 Javni sektor 1939.89 1249.47\n", "104 2018M05 Javni sektor 1937.98 1245.43\n", "106 2018M06 Javni sektor 1945.78 1249.62\n", "108 2018M07 Javni sektor 1937.25 1246.02\n", "110 2018M08 Javni sektor 1931.34 1245.08\n", "112 2018M09 Javni sektor 1909.41 1230.80\n", "114 2018M10 Javni sektor 1936.00 1245.44\n", "116 2018M11 Javni sektor 1989.90 1283.63\n", "118 2018M12 Javni sektor 2072.77 1337.07\n", "120 2019M01 Javni sektor 2031.75 1298.44\n", "122 2019M02 Javni sektor 1991.14 1276.17\n", "124 2019M03 Javni sektor 2047.39 1311.29\n", "126 2019M04 Javni sektor 2022.80 1292.73\n", "128 2019M05 Javni sektor 2040.59 1302.29\n", "130 2019M06 Javni sektor 2048.48 1306.80\n", "132 2019M07 Javni sektor 2044.13 1304.40\n", "134 2019M08 Javni sektor 2018.64 1292.79\n", "136 2019M09 Javni sektor 2021.58 1292.34\n", "138 2019M10 Javni sektor 2031.57 1297.15\n", "140 2019M11 Javni sektor 2135.61 1361.46\n", "142 2019M12 Javni sektor 2174.57 1408.77\n", "144 2020M01 Javni sektor 2096.96 1351.52\n", "146 2020M02 Javni sektor 2055.48 1327.33\n", "\n", "[74 rows x 4 columns]" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df['sektor']=='Javni sektor']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Zdaj lahko v ločeni tabeli shranimo podatke za javni in zasebni sektor" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "df_javni = df[df[\"sektor\"] == \"Javni sektor\"]\n", "df_zasebni = df[df[\"sektor\"] == \"Zasebni sektor\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "in pogledamo statistiko za vsak sektor posebej:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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brutoneto
count74.00000074.000000
mean1886.8506761218.003243
std106.50959463.695605
min1741.4400001133.470000
25%1794.5575001161.612500
50%1866.1950001204.535000
75%1944.3075001249.582500
max2174.5700001408.770000
\n", "
" ], "text/plain": [ " bruto neto\n", "count 74.000000 74.000000\n", "mean 1886.850676 1218.003243\n", "std 106.509594 63.695605\n", "min 1741.440000 1133.470000\n", "25% 1794.557500 1161.612500\n", "50% 1866.195000 1204.535000\n", "75% 1944.307500 1249.582500\n", "max 2174.570000 1408.770000" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_javni.describe()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
brutoneto
count74.00000074.000000
mean1502.097162981.401216
std93.49404459.959989
min1393.050000915.740000
25%1421.515000931.450000
50%1471.790000962.165000
75%1569.2375001018.832500
max1790.5200001177.510000
\n", "
" ], "text/plain": [ " bruto neto\n", "count 74.000000 74.000000\n", "mean 1502.097162 981.401216\n", "std 93.494044 59.959989\n", "min 1393.050000 915.740000\n", "25% 1421.515000 931.450000\n", "50% 1471.790000 962.165000\n", "75% 1569.237500 1018.832500\n", "max 1790.520000 1177.510000" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_zasebni.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Risanje grafov\n", "Tudi risanje grafov postane zdaj bolj enostavno. Namesto preko modula `Matplotlib` lahko zdaj funkcijo `plot` pokličemo kar preko tabele `dataframe`:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df_javni.plot()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Podamo lahko kateri podatki naj se izrišejo in kaj naj gre na posamezno os:" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df_javni.plot(x='mesec', y=['bruto','neto'])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Če bi želeli na isti graf risati iz različnih tabel, moramo to posebej povedati preko argumenta `ax`" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = plt.gca() # najprej pridobimo trenutne osi za risanje - te bomo potem podali pri risanju\n", "df_javni.plot(x='mesec', y=['bruto','neto'], ax=ax)\n", "df_zasebni.plot(x='mesec', y=['bruto','neto'], ax=ax)\n", "plt.legend(['Javni bruto', 'Javni neto', 'Zasebni bruto', 'Zasebni neto'])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Izvoz podatkov\n", "Podatke lahko tudi izvozimo. Izvoz v obliki CSV lahko izvršimo preko metode `to_csv`. Pri tem je ponavadi smiselno argument `index` postaviti na vrednost `False`, saj bomo sicer v datoteko izvozili tudi oznake stolpcev (`index`):" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "df_javni.to_csv(\"javni.csv\", index=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Izvoz (in kasneje uvoz) bi lahko naredili tudi preko datotek orodja Excel. V tem primeru uporabimo metodo `to_excel` (oziroma `read_excel`):" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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mesecsektorbrutoneto
02014M01Javni sektor1758.501151.30
12014M02Javni sektor1745.631136.41
22014M03Javni sektor1741.441133.47
32014M04Javni sektor1771.341151.12
42014M05Javni sektor1777.091153.05
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" ], "text/plain": [ " mesec sektor bruto neto\n", "0 2014M01 Javni sektor 1758.50 1151.30\n", "1 2014M02 Javni sektor 1745.63 1136.41\n", "2 2014M03 Javni sektor 1741.44 1133.47\n", "3 2014M04 Javni sektor 1771.34 1151.12\n", "4 2014M05 Javni sektor 1777.09 1153.05" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_javni.to_excel(\"javni.xlsx\", index=False)\n", "df_javni2 = pd.read_excel(\"javni.xlsx\")\n", "df_javni2.head()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3.7.3 64-bit ('base': conda)", "language": "python", "name": "python37364bitbasecondacd385dda59854559b44e3f82ede14f15" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }